Indices for Credibility Trending, Monitoring, and Lead Generation

ABSTRACT

Some embodiments provide a credibility system that computes credibility scores to quantify the credibility of different businesses and to coalesce the generated credibility scores into various indices. The indices comparatively present the credibility of a particular business relative to other businesses along one or more dimensions. Based on the indices, the system identifies trends in the credibility of a particular business. The system derives preliminary credibility for a new business for which credibility data has not yet been obtained based on credibility that has been previously established for other businesses in an index associated with the new business. The system provides automated services for monitoring credibility of a business and for generating alerts to notify the business that its credibility has reached various thresholds. The system identifies business practices that improve upon or adversely affect the credibility of a particular business.

CLAIM OF BENEFIT TO RELATED APPLICATIONS

This application is a continuation of U.S. nonprovisional applicationSer. No. 13/456,170, entitled “Indices for Credibility Trending,Monitoring, and Lead Generation”, filed Apr. 25, 2012 which claims thebenefit of U.S. provisional application 61/479,823, entitled“Credibility and Credit Indices and Derived Uses for Trending,Predictive Forecasting, Lead Generation, and Event Accounting”, filedApr. 27, 2011. The contents of application Ser. No. 13/456,170 and61/479,823 are hereby incorporated by reference.

TECHNICAL FIELD

The present invention pertains to systems, methods, and processes forenabling businesses to determine, communicate, and manage theircredibility.

BACKGROUND

Credibility is a measure of the trustworthiness, reputation, and beliefin an entity. Credibility may be derived from subjective and objectivecomponents relating to the services and goods that are provided by theentity. Credibility is built over time through the individualexperiences of clients and others who engage in commercial transactionswith the entity. These experiences are conveyed by word-of-mouth and arerecorded for others to view in various print, audio, visual, or digital(online) mass distribution mediums. For example, the reviews section ofthe newspaper stores the experiences of food and entertainment criticsand websites, such as www.yelp.com, www.citysearch.com, www.zagat.com,and www.amazon.com, provide an online medium that records theexperiences of individual consumers and professional critics in analways-on and readily available medium for others to view.

For the small business, credibility is a critical factor in determiningits day-to-day success. Specifically, whether a client leaves satisfiedwith a service or a product that has been purchased from the smallbusiness is instrumental in determining whether that client will be arepeat customer or whether that client will positively impact thecredibility of the small business by publishing reviews to encourageothers to visit the small business. A sufficient number of good clientexperiences that are recorded to the various mass distribution mediumsbeneficially increase the exposure of the small business, therebyresulting in better chances of growth, success, and profitability.Conversely, a sufficient number of bad client experiences can doom asmall business by dissuading potential clientele from engaging incommercial transactions with the small business. The success of thesmall business is therefore predicated more on credibility than on otherfactors such as business creditworthiness.

Due to the inherent partial subjective nature of credibility,credibility has long been a measure that is difficult to quantify.Instead, credibility has existed as an unreliable and inconsistent setof independent credibility data where the viewer of that credibilitydata is left to quantify the credibility of a business based on his/herown analysis. For example, users access websites such as www.yelp.com,www.citysearch.com, www.zagat.com, www.amazon.com, etc. to obtaincredibility data in the form of quantitative ratings, qualitativereviews, and other data about an entity from which to derive anindependent opinion of the credibility of that entity. Accordingly,different users will come to different conclusions about the credibilityof an entity even when provided the same set of credibility data.

While credibility data exists in many forms and in many different massdistribution mediums, there is currently no service that accurately,readily, and consistently quantifies that credibility data.Specifically, an online user can visit a website, such as www.yelp.com,view credibility data for a particular business that was submitted byhundreds of other users, and analyze that credibility data to derive afirst measure of credibility for that business. The same user can thenvisit a different website, such as www.citysearch.com, view differentcredibility data for the particular business that was submitted byhundreds of other users, and analyze that credibility data to derive asecond measure of credibility for that business that is inconsistentwith the first measure of credibility derived from the credibility datathat was obtained from www.yelp.com. Similarly, a different user canalso visit www.yelp.com, view the credibility data for the particularbusiness, and analyze that credibility data to derive a third measure ofcredibility for that business that is inconsistent with the measurederived by the first user from the same credibility data that isavailable at www.yelp.com, because the analysis that was employed byeach user was subject to different biases, interests, interpretation,importance, etc.

Accordingly, there is a need to standardize measures of credibility fordifferent businesses based on aggregate credibility data that isavailable at different credibility data sources. There is a need forsuch standardization to provide consistent, comparable, and easy tounderstand quantitative measures such that individual analytic biasesand interpretation are eliminated, credibility derived for each businessis derived according to the same set of rules and processes, andcredibility of one business can be compared with the credibility ofanother business where the other business is a competitor, in the samefield, in a different field, in the same region, etc.

SUMMARY OF THE INVENTION

It is an object of the present invention to define methods, systems, andcomputer software products for generating a tangible asset in the formof a standardized credibility score or credibility report thatquantifiably measures business credibility based on a variety of datasources and credibility data that includes quantitative data,qualitative data, and other data related to other credibilitydimensions. It is further an object to coalesce the generatedcredibility scores or reports into various indices. It is an object toutilize the indices to comparatively present the credibility of aparticular business relative to other businesses that are associatedwith the particular business along one or more dimensions. It is furtheran object to utilize the indices to identify trends in the credibilityof a particular business by comparing the credibility of the particularbusiness with the credibility of other businesses associated with theindices. It is further an object to derive preliminary credibility for anew business for which credibility data has not yet been obtained basedon credibility that has been previously established for other businessesin an index associated with the new business. It is further an object toprovide automated services for monitoring credibility of a business andfor generating alerts or other notifications to notify the business thatits credibility has exceeded or fallen below one or more thresholds thathave been set for that business, where the thresholds identifycredibility levels of particular importance to the business. It isfurther an object to utilize the indices for purposes of identifyingbusiness practices that improve upon or adversely affect the credibilityof a particular business. It is further an object to utilize the indicesfor purposes of identifying new partnerships that can improve upon thecredibility of a particular business and established partnerships thatadversely affect the credibility of that particular business.

Accordingly, some embodiments provide a credibility scoring andreporting system and methods. The credibility scoring and reportingsystem includes a master data manager, database, reporting engine, andinterface portal. The master data manager aggregates from multiple datasources qualitative credibility data, quantitative credibility data, andother data related to one or more entities. The master data managermatches the aggregated data to an appropriate entity to which the datarelates. The reporting engine performs natural language processing overthe qualitative credibility data to convert the qualitative credibilitydata into numerical measures that quantifiably represent the qualitativecredibility data. The quantitative measures and credibility data arethen filtered to remove abnormalities, to adjust weighting wheredesired, and to normalize the quantitative measures. For a particularentity, the reporting engine compiles the quantitative measures thatrelate to the particular entity into a credibility score. In someembodiments, a credibility report is generated to detail the derivationof the credibility score with relevant credibility data. In someembodiments, the credibility report also suggests actions for how theentity can improve upon its credibility score. Using the interfaceportal, businesses and individuals can purchase and view the credibilityscores and/or credibility reports while also engaging and interactingwith the credibility scoring and reporting system. Specifically, userscan submit credibility data and correct mismatches between credibilitydata and incorrect entities.

In some embodiments, the credibility scoring and reporting system isenhanced with an indexer. The indexer aggregates credibility scores formultiple entities that are related based on adjustable criteria. In someembodiments, the indexer also aggregates credibility data or credibilityreports of those related entities. The aggregated scores are compiledinto one or more indices. Each index of the indices comparativelypresents credibility of each of the entities that are associated withthat index. Different indices comparatively present credibility ofdifferent sets of entities that are related based on different criteriaassociated with each of the indices. From the various indices, users areable to quickly determine how the credibility of a given entity measuresin relation to its competitors, entities in related fields, entities insimilar geographic regions, or other criteria. In some embodiments, theindices are presented to the users through the interface portal with oneor more interactive tools. The interactive tools allow the users theability to on-the-fly adjust the criteria for the displayed index and toquickly obtain access to different indices related to a given entity.

In some embodiments, the indexer links the indices to those entitiesthat are related with an index. The indices and the associated links arestored to the database. Accordingly when a user searches for aparticular entity using the interface portal, the user will be providedaccess to one or more of the indices that are associated with thatparticular entity.

In some embodiments, the indexer performs analysis on the indices thatare associated with each entity in order to identify trends thatforecast the future credibility for those entities. These trends mayrelate to macro credibility influences that effect entities associatedwith the analyzed indices. Then, based on the identified trends, theindexer may forecast future or expected fluctuations to the credibilityof the entity or entities that are associated with a particular index.

The analysis further identifies, from the indices, business practices ofa particular entity that are proven to be successful or unsuccessful interms of positively or negatively affecting the credibility for thatparticular entity. The indexer automatically identifies successful orbeneficial business practices of the particular entity by identifyingother entities having good credibility in the indices that areassociated with the particular entity and by then identifyingcommonality between the credibility data of the particular entity andthe credibility data of the identified entities. Similarly, the indexerautomatically identifies unsuccessful or detrimental business practicesof the particular entity by identifying other entities having poorcredibility in the indices that are associated with the particularentity and by then identifying commonality between the credibility dataof the particular entity and the credibility data of the identifiedentities. This information provides the particular entity with targetedinformation from which it can identify specific practices that can beadjusted in order to correct and improve its credibility, and derivedcredibility score, thereby improving its standing in the variousindices. In some embodiments, the indexer analyzes the set of indicesthat are associated with the particular business in order to identifysuccessful and unsuccessful business practices in use by other entitiesand that can be suggested to the particular entity to improve itscredibility. Some such successful business practices are identified bydetecting commonality in the credibility data for the entities havingthe highest credibility scores in the indices associated with theparticular entity and some such unsuccessful business practice areidentified by detecting commonality in the credibility data for theentities having the lowest credibility scores in the indices associatedwith the particular entity.

Identification of these successful and unsuccessful business practicesfacilitates predictive credibility scoring by the indexer. Whenperforming predictive credibility scoring, the indexer models howchanges to various business practices of the particular entity willaffect the credibility score of that particular entity in the future. Inthis manner, the indexer discretely identifies steps that the particularentity can undertake to rectify or improve its credibility score whilealso discretely identifying what amount of improvement the particularentity is likely to see should those steps be performed.

In some embodiments, the indexer leverages the indices that areassociated with a particular entity in order to generate leadsidentifying partnerships that if established by the particular entitymay improve the credibility score for that particular entity. In somesuch embodiments, the indexer generates leads by identifying topperforming entities in an index associated with the particular entityand then identifying partnerships used by those top performing entitiesas leads. These partnerships may include partnerships with suppliers,manufacturers, financiers, marketing agencies, contractors, etc. andthat are established by the top performing entities. Similarly, theindexer can identify partnerships of the particular entity that arebeneficial and detrimental to its credibility score by comparing thepartnerships that the particular entity has with those of the topperforming entities. For example, the indexer identifies a partssupplier that is a partner of several entities having poor credibilityin the indices associated with a particular entity of interest and thatparts supplier is also a partner of the particular entity. Byidentifying this parts supplier, the indexer may identify a partnershipthat adversely affects the credibility of the particular entity, therebyindicating that the credibility of the particular entity can potentiallybe improved by partnering with a more credible parts supplier.

In some embodiments, the indexer forecasts potential fluctuations to thecredibility of an entity based on observed micro and macro events. Theseevents can have a bearing on whether demand for a good may increase andwhether supply for a part may decrease as some examples. Based on theidentification of these events and the forecasted change to thecredibility of the entity, the entity can take appropriate steps toaddress the fluctuations that are caused by the events and therebypreempt or proactively confront any such changes to the entity'scredibility.

In some embodiments, the indexer utilizes the indices to derive apreliminary credibility score for a new entity that has registered withthe credibility system and for which credibility data has not yet beenaggregated or does not exist in sufficient quantities to derive acredibility score. The indexer adjusts the preliminary credibility scorebased on factors such as number of direct competitors, age of themarket, historic growth of the market, how “hot” the market is, etc.

In some embodiments, the indexer provides credibility management andmonitoring. Using the interface portal, entities can set one or morecredibility score thresholds. When the credibility score for aparticular entity satisfies a particular set threshold, the particularentity is alerted or otherwise notified. The particular entity canrespond in kind to rectify a falling credibility score or identifywhether changes in business practices, marketing, partnerships, etc.have had a desired effect on the credibility of the particular entity.Once the thresholds are set, the monitoring occurs automatically withoutthe need for the particular entity to continually and manually check thescore itself.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to achieve a better understanding of the nature of the presentinvention a preferred embodiment of the credibility scoring andreporting system and methods will now be described, by way of exampleonly, with reference to the accompanying drawings in which:

FIG. 1 presents a process performed by the credibility scoring andreporting system to generate a credibility score and credibility reportin accordance with some embodiments.

FIG. 2 presents some components of the credibility scoring and reportingsystem of some embodiments.

FIG. 3 illustrates components of the master data manager in accordancewith some embodiments.

FIG. 4 presents a flow diagram for the matching process that isperformed by the master data manager of some embodiments.

FIG. 5 illustrates an exemplary data structure for storing thecredibility scoring information.

FIG. 6 illustrates some components of the reporting engine forgenerating credibility scores and credibility reports in accordance withsome embodiments.

FIG. 7 presents a process performed by the NLP engine for identifyingrelationships between textual quantifiers and modified objects inaccordance with some embodiments.

FIG. 8 illustrates identifying textual quantifier and modified objectpairs in accordance with some embodiments.

FIG. 9 presents a process for deriving quantitative measures fromqualitative credibility data in accordance with some embodiments.

FIG. 10 illustrates mapping identified textual quantifier and modifiedobject pairs to a particular value in a scale of values in accordancewith some embodiments.

FIG. 11 presents a process performed by the scoring filters to filterthe quantitative measures and credibility data in accordance with someembodiments.

FIG. 12 illustrates a credibility report window within the interfaceportal in accordance with some embodiments.

FIG. 13 presents an alternative credibility report viewer in accordancewith some embodiments.

FIG. 14 illustrates the credibility scoring and reporting systemenhanced with an indexer.

FIG. 15 presents a process performed by the indexer to generate an indexin accordance with some embodiments.

FIG. 16 presents a set of indices that are linked to a particularbusiness in accordance with some embodiments.

FIG. 17 illustrates a zoomed-in view of an index that presents a plotteddistribution of all businesses that satisfy the dimension of the index.

FIG. 18 illustrates two interactive sliders associated with an indexthat is “keyed” to a particular business.

FIG. 19 illustrates a plotted distribution of credit scores that isillustrative of a credit index in accordance with some embodiments.

FIG. 20 illustrates using drill-down functionality to hierarchicallyaccess credit ratings of a particular businesses in accordance with someembodiments.

FIG. 21 presents a process performed by the indexer in order to identifya trend for a particular business in accordance with some embodiments.

FIG. 22 conceptually illustrates identifying a trend based oncomparative analysis between credibility of a particular business and aset of related indices.

FIG. 23 presents a process performed by the indexer to identify for aparticular business in accordance with some embodiments the successfuland unsuccessful business practices of its competitors or of relatedbusinesses.

FIG. 24 presents a process performed by the indexer for identifyingsuccessful and unsuccessful business practices of a particular businessin accordance with some embodiments.

FIG. 25 presents a process performed by the indexer for predicting thecredibility score contribution of a particular business practice to acredibility score in accordance with some embodiments

FIG. 26 presents one or more business practices and an averagecredibility score determine for a business practice in accordance withsome embodiments.

FIG. 27 conceptually illustrates using process 2500 to predict thecredibility contribution for a selected business practice in accordancewith some embodiments

FIG. 28 presents a process that is in accordance with some embodimentsand that is performed by the indexer to identify for a particular entitythe beneficial and detrimental partners of its competitors or of relatedentities.

FIG. 29 conceptually illustrates using process 2800 to identify afiltered listing of partners of top performing businesses in accordancewith some embodiments.

FIG. 30 presents a process for providing targeted information regardingpartners of a particular business in accordance with some embodiments.

FIG. 31 presents a process performed by the indexer to compute apreliminary credibility score for a new business in accordance with someembodiments.

FIG. 32 presents a process performed by the indexer to adjustcredibility of businesses based on micro and macro events in accordancewith some embodiments.

FIG. 33 presents a process performed by the credibility scoring andreporting system to passively monitor credibility of a business inaccordance with some embodiments.

FIG. 34 illustrates a computer system with which some embodiments areimplemented.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous details, examples, andembodiments of a credibility scoring and reporting system including theindexer and the associated methods are set forth and described. As oneskilled in the art would understand in light of the present description,the system and methods are not limited to the embodiments set forth, andthe system and methods may be practiced without some of the specificdetails and examples discussed. Also, reference is made to accompanyingfigures, which illustrate specific embodiments in which the inventioncan be practiced. It is to be understood that other embodiments can beused and structural changes can be made without departing from the scopeof the embodiments herein described.

I. Overview

For the small business, business credibility is an invaluable asset thatcan be used to identify which business practices have been successful,practices that have inhibited the success of the business, desiredimprovements by customers, where future growth opportunities lie, andchanges that can be made to improve the future growth and success of thebusiness. Today, business credibility exists as qualitative data and asnon-standardized quantitative measures that selectively gauge variousfactors relating to a business using different ranking systems. Thequalitative and non-standardized nature of credibility data results inan intangible asset for which baseline measurements do not exist,cross-comparisons cannot be made, and against which individual biasesand scarcity of information undermine the relevancy of the information.Consequently, businesses, especially small business, are unable toeffectively determine or evaluate their credibility in the marketplaceand future strategic decisions are misguided or inaccurate as a result.

To overcome these and other issues and to provide tangible assets thatquantifiably measure entity credibility, some embodiments provide acredibility scoring and reporting system. The credibility scoring andreporting system generates standardized credibility scores thatquantifiably measure entity credibility based on aggregated data frommultiple data sources and that present the credibility as a readilyidentifiable score that can be comparatively analyzed againstcredibility scores of competitors that are derived using the same systemand methods. In some embodiments, the credibility scoring and reportingsystem generates credibility reports that detail the derivation of thecredibility score for each entity. More specifically, the credibilityreport is a single tool from which a particular entity can identifypractices that have been successful, practices that have inhibited thesuccess of the entity, desired improvements by customers, where futuregrowth opportunities lie, and changes that can be made to improve thefuture growth and success of the entity.

FIG. 1 presents a process 100 performed by the credibility scoring andreporting system to generate a credibility score and credibility reportin accordance with some embodiments. The process begins by aggregating(at 110) qualitative and quantitative credibility data from multipledata sources. This includes collecting data from various online andoffline data sources through partner feeds, files, and manual inputs.The process matches (at 120) the aggregated data to the appropriateentities. The matched data for each entity is analyzed (at 130) toidentify qualitative credibility data from quantitative credibilitydata. The process performs natural language processing (at 140) over thequalitative credibility data to convert the qualitative credibility datainto quantitative measures. The derived quantitative measures for thequalitative credibility data and the other aggregated quantitativecredibility data are then subjected to the scoring filters that modify(at 150) the quantitative measures for abnormal and biased credibilitydata and that normalize the quantitative measures. The process produces(at 160) a credibility score by compiling the remaining normalizedquantitative measures.

The credibility score accurately represents the credibility of a givenentity, because (i) the credibility score is computed using data fromvaried data sources and is thus not dependent on or disproportionatelyaffected by any single data source, (ii) the credibility data isprocessed using algorithms that eliminate individual biases from theinterpretation of the qualitative credibility data, (iii) thecredibility data is processed using filters that eliminate biasedcredibility data while normalizing different quantitative measures, and(iv) by using the same methods and a consistent set of algorithms toproduce the credibility score for a plurality of entities, the producedcredibility scores are standardized and can be subjected to comparativeanalysis in order to determine how the credibility score of one entityranks relative to the credibility scores of competitors or otherentities. As a result, the credibility score can be sold as a tangibleasset to those entities interested in understanding their owncredibility.

In some embodiments, the process also generates (at 170) a credibilityreport as a separate tangible asset for entities interested inunderstanding the derivation of their credibility score and how toimprove their credibility score. In some embodiments, the credibilityreport presents relevant credibility data to identify the derivation ofthe credibility score. In some embodiments, the credibility report alsosuggests actions for how the entity can improve upon its credibilityscore.

Some embodiments provide an interface portal from which entities orother users can purchase and view the credibility scores and/orcredibility reports. Using these assets (i.e., credibility scores andcredibility reports), entities can formulate accurate and targetedbusiness objectives to improve their credibility and, more importantly,their likelihood for future growth and success. Entities will also haveaccess to the credibility scores of other entities. The credibilityscore can be used in this manner to guide clientele to crediblebusinesses and steer clientele away from entities providing a poorcustomer experience. Moreover, the credibility scores can serve toidentify entities with which a particular entity would want to partnerwith or form relationships with for future business transactions.Accordingly, there is incentive for entities to improve upon theircredibility scores as clientele and partners may be looking at the sameinformation when determining whether or not to conduct business with aparticular entity.

The portal further acts as a means by which entities can be directlyinvolved with the credibility scoring process. Specifically, using theinterface portal, entities can submit pertinent credibility data thatmay otherwise be unavailable from the data sources and can correctmismatched credibility data.

II. Credibility Scoring and Reporting System

FIG. 2 presents components of the credibility scoring and reportingsystem 205 of some embodiments. The credibility scoring and reportingsystem 205 includes (1) master data manager 210, (2) database 220, (3)reporting engine 230, and (4) interface portal 240. As one skilled inthe art would understand in light of the present description, thecredibility scoring and reporting system 205 may include othercomponents in addition to or instead of the enumerated components ofFIG. 2. The components 210-240 of FIG. 2 are not intended as anexhaustive listing, but rather as an exemplary set of components fordescriptive and presentation purposes. The overall system 205 isdesigned with modular plug-in components whereby new components orenhanced functionality can be incorporated within the overall system 205without having to modify existing components or functionality.

A. Master Data Manager

At present, an entity can attempt to determine its credibility byanalyzing credibility data at a particular data source to see whatothers are saying about the entity. Credibility obtained in this manneris deficient in many regards. Firstly, credibility that is derived fromone or a few data sources is deficient because a sufficient sampling ofcredibility cannot be obtained from a single data source or even from afew data sources. For example, a site that includes only two negativereviews about a particular entity may not accurately portray thecredibility of that particular entity when that particular entityservices thousands of individuals daily. Moreover, one or more of thedata sources may have biased data or outdated data thatdisproportionately impact the credibility of the entity. Secondly,credibility that is derived from one or a few data sources is deficientbecause each data source may contain information as to a particularaspect of the entity. As such, credibility derived from such few sourceswill not take into account the entirety of the entity's businessdealings and can thus be misleading. Thirdly, credibility is deficientwhen it is not comparatively applied across all entities, amongstcompetitors, or a particular field of business. For example, a criticalreviewer may identify a first entity as “poor performing” and identify asecond entity as “horribly performing”. When viewed separately, eachentity would be classified with poor credibility. However, withcomparative analysis, the first entity can be classified with bettercredibility than the second entity. Fourthly, credibility data fromdifferent reviewers or data sources is not standardized which opens thecredibility data to different interpretations and individual biases. Forexample, it is difficult to determine whether for the same entity a 3out of 5 ranking from www.yelp.com is equivalent to a 26 out of 30ranking on www.zagat.com. Similarly, a review that states the servicesof a first entity as “good” can be interpreted by the first entity as asuccessful or positive review, whereas the same review of “good” for asecond entity can be interpreted by the second entity as an averagereview from which services have to be improved upon.

To address these and other issues in deriving entity credibility, someembodiments include the master data manager 210. The master data manager210 interfaces with multiple data sources 250 and to automatedly acquirerelevant credibility data from these sources 250 at regular andcontinuous intervals. In so doing, the master data manager 210 removesthe deficiencies that result from an insufficient sample size, outdateddata, and lack of comparative data.

FIG. 3 illustrates components of the master data manager 210 inaccordance with some embodiments. The master data manager 210 includesvarious plug-in interface modules 310 (including plug-in 320), matchingprocess 330, and database 340 storing a set of matching algorithms.Access to the master data manager 210 is provided through the interfaceportal 240 of FIG. 2.

The master data manager 210 aggregates data from various data sourcesthrough the plug-in interface modules 310 (including 320) and throughthe interface portal 240. Each plug-in interface module 310 isconfigured to automatically interface with one or more data sources inorder to extract credibility data from those data sources. In someembodiments, each plug-in interface module 310 is configured withcommunication protocols, scripts, and account information to access oneor more data sources. Additionally, each plug-in interface module 310may be configured with data crawling functionality to extractcredibility data from one or more data sources. A particular plug-ininterface module navigates through a particular data source in order tolocate the credibility data. In one illustrated example, the master datamanager 210 includes a particular plug-in interface module 320 to thewebsite www.yelp.com. This interface module 320 can be configured withaccount information to access the www.yelp.com website and a datacrawler script to scan through and extract entity credibility datadirectly from the website. In some embodiments, partnership agreementsare established with the data sources, whereby the plug-in interfacemodules directly interface with one or more databases of the data sourcein order to extract the credibility data.

The extracted credibility data includes qualitative data andquantitative data about one or more entities. Qualitative data includescustomer and professional review data, blog content, and social mediacontent as some examples. Some data sources from which qualitative dataabout various entities may be acquired are internet websites such aswww.yelp.com, www.citysearch.com, www.zagat.com, www.gayot.com,www.facebook.com, and www.twitter.com. Accordingly, some embodiments ofthe master data manager 210 include a different plug-in interface module310 to extract the credibility data from each of those sites.Quantitative data includes credit and credibility data that isquantitatively measured using some scale, ranking, or rating. Somequantitative data sources include Dun & Bradstreet® and the BetterBusiness Bureau® (BBB). Some qualitative data sources may also includequantitative credibility data. For example, www.yelp.com includesqualitative data in the form of textual reviews and comments andquantitative data in the form of a 0 out of 5 rating system. Someembodiments of the master data manager 210 include a different plug-ininterface module 310 to extract quantitative data from the quantitativedata sources.

The plug-in interface modules 310 allow data from new data sources to beintegrated into the master data manager 210 without alteringfunctionality for any other plug-in interface module 310. Thismodularity allows the system to scale when additional or newer datasources are desired. Moreover, the plug-in interface modules 310 allowthe credibility data to automatically and continuously be acquired fromthese various data sources. In some embodiments, the aggregated dataincludes copied text, files, feeds, database records, and other digitalcontent.

Qualitative data and quantitative data may also be aggregated from othermediums including print publications (e.g., newspaper or magazinearticles), televised commentary, or radio commentary. In someembodiments, the data sources access the interface portal 240 in orderto provide their data directly to the master data manager 210. Forexample, relevant magazine articles may be uploaded or scanned andsubmitted through the interface portal 240 by the publisher.Publications and recordings may also be submitted by mail. An incentivefor the publisher to submit such information is that doing so mayincrease the exposure of the publisher. Specifically, the exposure mayincrease when submitted publications are included within the generatedcredibility reports of some embodiments.

Credibility data may also be submitted directly by an entity to themaster data manager 210. This is beneficial for small business entitiesthat are unknown to or otherwise ignored by the various data sources.Specifically, credibility data can be submitted through the interfaceportal 240 by the business owner and that data can be incorporated intothe credibility scores and credibility reports as soon as the databecomes available. In this manner, the business entity can be directlyinvolved with the credibility data aggregation process and need notdepend on other data sources to provide credibility data about thebusiness to the master data manager 210. For example, the Los AngelesCounty of Health issues health ratings to restaurants on a graded A, B,and C rating system. Should a restaurant receive a new rating, therestaurant business owner can submit the new rating to the master datamanager 210 through the interface portal 240 without waiting for a thirdparty data source to do so. A submission may be made via a webpage inwhich the submitting party identifies himself/herself and enters thedata as text or submits the data as files.

The master data manager 210 tags data that is aggregated using theplug-in modules 310 and data that is submitted through the interfaceportal 240 with one or more identifiers that identify the entity towhich the data relates. In some embodiments, the identifiers include oneor more of a name, phonetic name, address, unique identifier, phonenumber, email address, and Uniform Resource Locator (URL) as someexamples. For automatically aggregated credibility data, the plug-inmodules 310 tag the aggregated credibility data with whatever availableidentifiers are associated with the credibility data at the data source.For example, the www.yelp.com site groups reviews and ranking (i.e.,credibility data) for a particular entity on a page that includescontact information about the entity (e.g., name, address, telephonenumber, website, etc.). For credibility data that is submitted throughthe interface portal 240, the submitting party will first be required tocreate a user account that includes various identifiers that are to betagged with the credibility data that is sent by that party.

In some cases, the tagged identifiers do not uniquely or correctlyidentify the entity that the data is to be associated with. This mayoccur when an entity operates under multiple different names, phonenumbers, addresses, URLs, etc. Accordingly, the master data manager 210includes matching process 330 that matches the aggregated data to anappropriate entity using a set of matching algorithms from the matchingalgorithms database 340. To further ensure the integrity and quality ofthe data matching, some embodiments allow for the entities themselvesand community to be involved in the matching process 330.

FIG. 4 presents a flow diagram for the matching process 330 that isperformed by the master data manager of some embodiments. The matchingprocess 330 involves tagged credibility data 410, an automated matchingprocess 420, a first database 430, a second database 440, interfaceportal 240, owners 470, user community 480, correction process 490, andmatching algorithms database 340.

The matching process 330 begins when tagged credibility data 410 ispassed to the automated matching process 420. The automated matchingprocess 420 uses various matching algorithms from the matchingalgorithms database 340 to match the credibility data 410 with anappropriate entity. Specifically, the credibility data 410 is associatedwith an identifier that uniquely identifies the appropriate entity. Whena match is made, the credibility data is stored to the first database430 using the unique identifier of the entity to which the credibilitydata is matched. In some embodiments, the first database 430 is thedatabase 220 of FIG. 2. In some embodiments, the unique identifier isreferred to as a credibility identifier. As will be described below, thecredibility identifier may be one or more numeric or alphanumeric valuesthat identify the entity.

In addition to matching the data to the appropriate entity, theautomated matching process 420 may also perform name standardization andverification, address standardization and verification, phonetic namematching, configurable matching weights, and multi-pass error suspensereduction. In some embodiments, the automated matching process 420executes other matching algorithms that match multiple entities to eachother if ownership, partnership, or other relationships are suspected.For example, the automated matching process 420 determines whether theAcme Store in New York is the same entity as the Acme Store inPhiladelphia, whether variations in the spelling of the word Acme (e.g.,“Acme”, “Acmi”, “Akme”, “Ackme”, etc.) relates to the same entity ordifferent entities, or whether “Acme Store”, “Acme Corporation”, and“Acme Inc.” relate to the same entity or different entities. Suchmatching is of particular importance when ascertaining credibility forentities with both a digital presence (i.e., online presence) and anactual presence. For instance, offline credit data may be associatedwith a business entity with the name of “Acme Corporation” and that sameentity may have online credibility data that is associated with the nameof “Acme Pizza Shop”.

However, the matching process 330 may be unable to automatically matchsome of the credibility data to an entity when there is insufficientinformation within the tags to find an accurate or suitable match.Unmatched credibility data is stored to the second database 440. Thesecond database 440 is a temporary storage area that suspends unmatchedcredibility data until the data is discarded, manually matched by owners470 (i.e., business entity owners), or manually matched by users in thecommunity 480.

The interface portal 240 of FIG. 2 allows owners 470 and a community ofusers 480 to become involved in the matching process 330. In someembodiments, the interface portal 240 is a website through which owners470 gain access to the matching process 330 and the databases 430 and440. Through the interface portal 240, owners 470 can claim theiraccounts and verify themselves as a particular entity. Thereafter, theowners 470 can control matching errors, detect identity fraud, andmonitor the integrity of their credibility score. Specifically, owners470 can identify matching errors in the first database 430 and confirm,decline, or suggest matches for credibility data that has been suspendedto the second database 440. Through the interface portal 240, owners 470can address credibility issues in real-time. In some embodiments, owners470 include agents or representatives of the business entity that arepermitted access to the business entity account in the credibilityscoring and reporting system.

In some embodiments, the interface portal 240 also provides users accessto the matching process 330 through a plug-in. The plug-in can beutilized on any website where credibility data is found. In someembodiments, the plug-in is for external websites that wish toseamlessly integrate the backend of credibility data suppliers to thecredibility scoring and reporting system. In this manner, an entity canown and manage the review of credibility data itself. Accordingly,whenever a user in the community 480 or owner 470 spots an incorrectmatch or issues with credibility data, they can interact with that datathrough the plug-in This allows for community 480 interaction wherebyother users help improve matching results.

When an improper match is flagged for review or a new match issuggested, it is passed to the correction process 490 for verification.In some embodiments, the correction process 490 includes automatedcorrection verification and manual correction verification. Automatedcorrection verification can be performed by comparing the flaggedcredibility data against known entity account information or othercredibility data that has been matched to a particular entity. Approvedcorrections are entered into the first database 430. Disapprovedcorrections are ignored.

In some embodiments, adjustments may be made to improve the matchingaccuracy of the matching algorithms in the matching algorithm database340 based on the approved corrections. In this manner, the matchingprocess 330 learns from prior mistakes and makes changes to thealgorithms in a manner that improves the accuracy of future matches.

B. Database

Referring back to FIG. 2, the database 220 stores various informationpertaining to the credibility scoring of each entity using the uniqueidentifier that is assigned to each entity. FIG. 5 illustrates anexemplary data structure 510 for storing the credibility scoringinformation. The data structure 510 includes unique identifier 515,contact elements 520, credibility elements 530, and entity elements 540.

As before, the unique identifier 515 uniquely identifies each entity.The contact elements 520 store one or more names, addresses,identifiers, phone numbers, email addresses, and URLs that identify anentity and that are used to match aggregated and tagged credibility datato a particular entity. The credibility fields 530 store the aggregatedand matched qualitative and quantitative credibility data. Additionally,the credibility fields 530 may store generated credibility scores andcredibility reports that are linked to the unique identifier 515 of thedata structure 510. The entity elements 540 specify businessinformation, individual information, and relationship information.Business information may include business credit, financial information,suppliers, contractors, and other information provided by companies suchas Dun & Bradstreet. Individual information identifies individualsassociated with the business. Relationship information identifies theroles of the individuals in the business and the various businessorganization or structure. Individual information may be included toassist in the matching process and as factors that affect thecredibility score. For example, executives with proven records ofgrowing successful businesses can improve the credibility score for aparticular business and inexperienced executives or executives that haveled failing businesses could detrimentally affect the credibility scoreof the business.

Logically, the database 220 may include the databases 430 and 440 ofFIG. 4 and other databases referred to in the figures and in thisdocument. Physically, the database 220 may include one or more physicalstorage servers that are located at a single physical location or aredistributed across various geographic regions. The storage serversinclude one or more processors, network interfaces for networkedcommunications, and volatile and/or nonvolatile computer-readablestorage mediums, such as Random Access Memory (RAM), solid state diskdrives, or magnetic disk drives.

C. Reporting Engine

The reporting engine 230 accesses the database 220 to obtain credibilitydata from which to derive the credibility scores and credibility reportsfor various entities. In some embodiments, the reporting engine 230updates previously generated scores and reports when credibility scoresand reports for an entity have been previously generated and credibilitydata has changed or new credibility data is available in the database220. FIG. 6 illustrates some components of the reporting engine 230 forgenerating credibility scores and credibility reports in accordance withsome embodiments. The reporting engine 230 includes data analyzer 610,natural language processing (NLP) engine 620, scoring engine 625,scoring filters 630, credibility scoring aggregator 640, and reportgenerator 650. In some embodiments, the reporting engine 230 and itsvarious components 610-650 are implemented as a set of scripts ormachine implemented processes that execute sets of computerinstructions.

i. Data Analyzer

The data analyzer 610 interfaces with the database 220 in order toobtain aggregated credibility data for one or more entities. As notedabove, credibility data for a particular entity is stored to thedatabase 220 using a unique identifier. Accordingly, the data analyzer610 is provided with one or a list of unique identifiers for whichcredibility scores and reports are to be generated. The list of uniqueidentifiers may be provided by a system administrator or may begenerated on-the-fly based on requests that are submitted through theinterface portal. The data analyzer 610 uses the unique identifiers toretrieve the associated data from the database 220.

Once credibility data for a particular entity is retrieved from thedatabase 220, the data analyzer 610 analyzes that credibility data toidentify and separate qualitative credibility data from quantitativecredibility data. The data analyzer 610 uses pattern matching techniquesand character analysis to differentiate the qualitative credibility datafrom the quantitative credibility data. Qualitative credibility dataincludes data that is not described in terms of quantities, notnumerically measured, or is subjective. Text based reviews and commentsobtained from sites such as www.yelp.com and www.citysearch.com areexamples of qualitative data. Accordingly, the data analyzer 610identifies such text based reviews and classifies them as qualitativecredibility data. The data analyzer 610 passes identified qualitativedata to the NLP engine 620 and the scoring engine 625 for conversioninto quantitative measures. Conversely, quantitative credibility dataincludes data that is described in terms of quantities, is quantifiablymeasured, or is objective. A credit score, rating, or rankings that areconfined to a bounded scale (e.g., 0-5 stars) are examples ofquantitative data. Accordingly, the data analyzer 610 identifies thesescores, ratings, and rankings as quantitative credibility data. The dataanalyzer 610 passes identified quantitative data to the scoring filters630.

ii. NLP Engine

In some embodiments, the NLP engine 620 performs relationshipidentification on qualitative credibility data. Specifically, the NLPengine 620 identifies relationships between (i) textual quantifiers and(ii) modified objects.

In some embodiments, a textual quantifier includes adjectives or otherwords, phrases, and symbols from which quantitative measures can bederived. This includes words, phrases, or symbols that connote somedegree of positivity or negativity. The following set of words connotessimilar meaning albeit with different degrees: “good”, “very good”,“great”, “excellent”, and “best ever”. Textual quantifiers also includeadjectives for which different degree equivalents may or may not exist,such as: “helpful”, “knowledgeable”, “respectful”, “courteous”,“expensive”, “broken”, and “forgetful”. The above listings are anexemplary set of textual quantifiers and are not intended to be anexhaustive listing. A full listing of textual quantifiers are stored toa database that is accessed by the NLP engine 620. In this manner, theNLP engine 620 can scale to identify new and different textualquantifiers as needed.

In some embodiments, a modified object includes words, phrases, orsymbols that pertain to some aspect of an entity and that are modifiedby one or more textual quantifiers. In other words, the modified objectsprovide context to the textual quantifiers. For example, the statement“my overall experience at the Acme Store was good, but the service wasbad” contains two textual quantifiers “good” and “bad” and two modifiedobjects “overall experience” and “service”. The first modified object“overall experience” is modified by the textual quantifier “good”. Thesecond modified object “service” is modified by the textual quantifier“bad”. In some embodiments, a full listing of modified objects is storedin a database that is accessed by the NLP engine. Additionally,grammatical rules and other modified object identification rules may bestored to the database and used by the NLP engine to identify theobjects that are modified by various textual quantifiers.

FIG. 7 presents a process 700 performed by the NLP engine 620 foridentifying relationships between textual quantifiers and modifiedobjects in accordance with some embodiments. The process 700 begins whenthe NLP engine 620 receives (at 710) qualitative credibility data fromthe data analyzer 610. The process performs an initial pass through thecredibility data to identify (at 720) the textual quantifiers therein.During a second pass through, the process attempts to identify (at 730)a modified object for each of the textual quantifiers. Unmatched textualquantifiers or textual quantifiers that match to an object that does notrelate to some aspect of an entity are discarded. Matched pairs arepassed (at 740) to the scoring engine 625 for conversion intoquantitative measures and the process 700 ends. It should be apparentthat other natural language processing may be performed over thequalitative credibility data in order to facilitate the derivation ofquantitative measures from such data and that other such processing maybe utilized by the NLP engine 620.

FIG. 8 illustrates identifying textual quantifier and modified objectpairs in accordance with some embodiments. The figure illustratesqualitative credibility data 810 in the form of a review. The reviewtextually describes various user experiences with an entity. When passedto the NLP engine 620 for processing, the textual quantifiers andmodified objects of the review are identified. In this figure, thetextual quantifiers are indicated using the rectangular boxes (e.g.,820) and the modified objects (e.g., 830) are identified with circles.

iii. Scoring Engine

The NLP engine 620 passes the matched pairs of textual quantifiers andmodified objects to the scoring engine 625. The scoring engine 625converts each pair to a quantitative measure. FIG. 9 presents a process900 for deriving quantitative measures from qualitative credibility datain accordance with some embodiments. The process 900 begins when thescoring engine 625 receives from the NLP engine 620 qualitativecredibility data with identified pairs of textual quantifiers andmodified objects.

The process selects (at 910) a first identified textual quantifier andmodified object pair. Based on the modified object of the selected pair,the process identifies (at 920) a quantitative scale of values. In someembodiments, the scale of values determines a weight that is attributedto the particular modified object. Some modified objects are weightedmore heavily than others in order to have greater impact on thecredibility score. For example, from the statement “my overallexperience at the Acme Store was good, but the service was bad”, themodified object “overall experience” is weighted more heavily than themodified object “service”, because “service” relates to one aspect ofthe entity's credibility, whereas “overall experience” relates to theentity credibility as a whole. In some embodiments, the process uses themodified object as an index or hash into a table that identifies thecorresponding scale of values associated with that modified object.

Next, the process maps (at 930) the textual quantifier from theidentified pair to a particular value in the identified scale of valuesto derive a quantitative measure. In some embodiments, the mapping isperformed in conjunction with a conversion formula that outputs aparticular value when the textual quantifier and a scale of values areprovided as inputs. In some other embodiments, the textual quantifiermaps to a first value that is then adjusted according to the scale ofvalues identified by the modified object. For example, the textualquantifiers “good”, “very good”, “great”, “excellent”, and “best ever”map to values of 6, 7, 8, 9, and 10 respectively in an unadjusted scaleof 0-10. A modified object that is paired with the textual quantifier“great” may identify a scale of value ranging from 0-100. Accordingly,the value associated with the textual quantifier (i.e., 8) is adjustedper the identified scale to a value of 80.

The process determines (at 940) whether there are other identifiedtextual quantifier and modified object pairs associated with thecredibility data. If so, the process reverts to step 910 and selects thenext pair. Otherwise, the process passes (at 950) the mapped valuesalong with the associated credibility data to the scoring filters 630and the process 900 ends.

FIG. 10 illustrates mapping matched textual quantifier and modifiedobject pairs to a particular value in a scale of values in accordancewith some embodiments. As shown, for each identified textual quantifierand modified object pair, a scale of values (e.g., 1010 and 1020) isidentified to represent the relative weight or importance of thatmodified object to the overall credibility score. For example, the scaleof values 1010 ranges from 0-20 and the range of values 1020 ranges from0-3. This indicates that the modified object that is associated with thescale of values 1010 is weighted more heavily in the credibility scorethan the modified object that is associated with the scale of values1020. The textual quantifier for each identified pair is then mapped toa particular value in the scale of values (e.g., 1030 and 1040). Inlight of the present description, it should be apparent that thepresented scales are for exemplary purposes and that the scoring engine625 may utilize different scales for different modified objects.

In some embodiments, the reporting engine 230 monitors relationshipsbetween quantitative data and qualitative data to promote self-learningand adaptive scoring. Credibility data sources often provide aquantitative score that ranks or rates an entity on some quantitativescale (e.g., 0-5 stars) and an associated set of qualitative data thatcomments on or explains the quantitative score. Based on therelationship between the quantitative data and the qualitative data, thereporting engine 230 of some embodiments adaptively adjusts howquantitative measures are derived from qualitative data. Specifically,the reporting engine 230 adjusts (i) the scale of values provided tocertain modified objects found in qualitative data and (ii) the valuethat is selected in a scale of values for a particular textualquantifier that is associated with a modified object. For example, whena quantitative score of 5 out of 5 appears 75% of the time withqualitative data that includes the textual quantifier “good” and aquantitative score of 3 out of 5 appears 80% of the time withqualitative data that includes the textual quantifier “fine”, then thereporting engine 230 learns from these relationships to increase thequantifiable value for the “good” textual quantifier and decrease thequantifiable value for the “fine” textual quantifier.

In some embodiments, the reporting engine 230 monitors relationshipsbetween the various textual quantifiers and modified objects in thequalitative data to promote self-learning and adaptive scoring.Specifically, the reporting engine 230 adjusts the scale of valuesassociated with a particular modified object based on the frequency withwhich that modified object appears in the qualitative data. Similarly,the reporting engine 230 can adjust the selected value associated with aparticular textual quantifier based on the frequency with which thattextual quantifier appears in the qualitative data. These frequencymeasurements can be made on a per entity basis, on a businesssub-classification (e.g., fast food restaurant, fine dining restaurant,and family restaurant), or on a field of business basis (e.g.,restaurants, clothing stores, and electronic stores). For example, whenthe phrase “the food was” appears in 75% of user reviews that areassociated with a particular entity and the phrase “the waiter was”appears in 10% of user reviews that are associated with that particularentity, then the reporting engine 230 can provide greater weight to thescale of values that is associated with the modified object “food” thanthe scale of values that is associated with the modified object“waiter”. In this manner, the credibility score derived from thequalitative data can better account for those factors that usersfrequently comment on while reducing the impact that other rarelymentioned factors have on the credibility score.

In summary, the scale of values for certain modified objects and theselected value from the scale of values for the associated textualquantifier can be adaptively adjusted based on the correspondencebetween quantitative data that is associated with qualitative data andbased on the relative frequency that a particular textual quantifier ormodified object is used with reference to a particular entity,sub-classification of a business, or field-of-business.

iv. Scoring Filters

In some embodiments, the scoring filters 630 filter the quantitativemeasures and the credibility data before producing the credibilityscore. In some embodiments, the scoring filters 630 include executableprocesses that incorporate different pattern matching criteria toidentify which quantitative measures or which credibility data to filterbased on what conditions. Each scoring filter may be specific to one ormore types of credibility data. As such, the scoring filters areselectively applied to the credibility data based on the type ofcredibility data.

FIG. 11 presents a process 1100 performed by the scoring filters 630 tofilter the quantitative measures and credibility data in accordance withsome embodiments. The process begins by using a set of filters to remove(at 1110) quantitative measures obtained from outlying, abnormal, andbiased credibility data. This includes removing quantitative measuresthat originate from credibility data that is irrelevant to the entity atissue. For example, removing a quantitative measure that originates fromcredibility data that states various complaints with regards todifficulty in setting up equipment purchased from a store when settingup the equipment is unrelated to the goods and services offered by thestore. Other filters may be defined to analyze credibility data inconjunction with information about the party submitting the review. Forexample, a filter may be defined that analyzes demographic informationin association with credibility data. This is useful when a businessentity is geared towards specific clientele and the party submitting thereview does not fall into that classification of clientele. Accordingly,a scoring filter can be defined to remove such quantitative measures.Other quantitative measures from anonymous reviewers or credibility datathat relates to extreme cases or irregular events can also be removed.

Next, the process uses a set of filters to adjust (at 1120)inconsistencies in the quantitative measures for the remainingcredibility data. For example, different reviewers may each give aparticular entity a three out of five rating, but in the associatedcomments a first reviewer may provide positive feedback while a secondreviewer may provide negative feedback. In such cases, filters can bedefined to increase the quantitative measure provided by the firstreviewer based on the positive feedback and decrease the quantitativemeasure provided by the second reviewer based on the negative feedback.

The process uses a set of filters to normalize (at 1130) thequantitative measures for the remaining credibility data. Normalizationincludes adjusting the scaling of quantitative measures. In someembodiments, the quantitative measures for qualitative credibility datathat are derived by the scoring engine 625 will not requirenormalization. However, quantitative measures originating fromquantitative credibility data may require normalization. For instance,quantitative measures of quantitative credibility data obtained from afirst data source (e.g., www.yelp.com) may include a rating that is outof five stars and quantitative measures of quantitative credibility dataobtained from a second data source (e.g., www.zagat.com) may include apoint scale of 0-30 points. In some embodiments, the process normalizesthese quantitative measures to a uniform scale of values (e.g., 0-100).In some other embodiments, the process normalizes these quantitativemeasures with disproportionate weighting such that quantitative measuresobtained from credibility data of a more trusted data source areprovided more weight than quantitative measures obtained fromcredibility data of a less trusted data source. Disproportionateweighting is also used to limit the impact stale credibility data hasover the credibility score. Specifically, quantitative measures fromolder credibility data are normalized with less weighting thanquantitative measure from newer credibility data. Different scoringfilters may be defined to implement these and other weighting criteria.

The process stores (at 1140) the filtered quantitative measures data tothe database 220 and the process ends. In some embodiments, the processdirectly passes the filtered quantitative measures to the credibilityscoring aggregator 640 of the reporting engine 230.

v. Credibility Scoring Aggregator

The credibility scoring aggregator 640 produces a credibility score fora particular entity based on normalized quantitative measures for thatparticular entity. In some embodiments, the credibility score is anumerical value that is bounded in a range that represents a lack ofcredibility at one end and full credibility at another end, wherecredibility accounts for successes of various practices, customersatisfaction, performance relative to competitors, growth potential,etc. In some embodiments, the credibility score may be encoded tospecify different credibility aspects with different digits. Forexample, the first three digits of a six digit score specify a creditscore and the last three digits of the six digit score specify thecredibility score. In some embodiments, the credibility score is a setof scores with each score representing a different component ofcredibility. For example, the credibility score may comprise a creditscore, a review score, and a rating score where the review score iscompiled from quantitative measures derived from the aggregatedqualitative data and the rating score is compiled from the normalizedquantitative measures within the aggregated quantitative data. It shouldbe apparent to one of ordinary skill in the art that the credibilityscore can be formatted in any number of other ways, such as a set offormatted characters or as a set of formatted alphanumeric characters.

To produce the credibility score, the credibility scoring aggregator 640aggregates any filtered and normalized quantitative measures for aparticular entity from the database 220 or from the scoring filters 630.The credibility scoring aggregator 640 then uses one or more proprietaryalgorithms to factor together the quantitative measures to produce thecredibility score. This may include averaging, summing, or usingproprietary formulas to produce the credibility score from theaggregated set of quantitative measures. These algorithms allow for acredibility score to be computed with any number of availablequantitative measures. The produced credibility score is then storedback to the database 220 where it is associated with the particularentity.

From the interface portal 240 of FIG. 2, entities can access and viewtheir credibility scores. In some embodiments, the credibility score isupdated and presented in real-time. In some embodiments, the credibilityscore is a tangible asset that users and entities purchase beforeprovided access to the credibility score. Users and entities canpurchase a onetime viewing of the credibility score or can purchase asubscription plan that allows them to view credibility scores anytimeduring a particular subscription cycle (e.g., monthly, yearly, etc.).Users and entities can also purchase and view their credibility reportsor purchase credibility scores and reports for other entities that theymay be interested in doing business with or to see a competitor'scredibility.

vi. Report Generator

The report generator 650 operates in conjunction with the credibilityscoring aggregator 640. In some embodiments, the report generator 650 istasked with (1) producing reports that detail how a credibility scorewas derived, (2) organizing aggregated credibility data, derivedcredibility data, and referenced credibility data pertaining to anentity, and (3) organizing ancillary data for informative anddescriptive identification of the entity. Such data is aggregated by themaster data manager 220. As will be described below, credibility dataand other ancillary informative or descriptive data may include maps,news, identification information, financial data, photos, videos, socialnetwork content, and network partnerships associated with the entity.All such data serves as supplemental credibility data that creates aholistic and multi-dimensional view of the entity's credibility.

In some embodiments, the credibility report is a data structure that isstored to the database 220 of FIG. 2. Organizing the aggregated dataincludes grouping related credibility data into different datasets thatrepresent different dimensions of credibility. For example, groupingqualitative data that is used to derive a review score to a firstdataset that represents a first dimension of credibility, groupingquantitative credibility data that is used to derive a rating score intoa second dataset that represents a second dimension, and grouping socialnetwork content to a third dataset that represents a third dimension ofcredibility. In some embodiments, a single dataset may be used torepresent two or more dimensions of credibility. For example,qualitative data grouped to a dataset may be associated with acredibility scoring dimension and a rating dimension.

Instead of storing all data to the data structure of the credibilityreport, some of the data for the credibility report may be obtainedon-the-fly from a data source partner. In such cases, the reportgenerator 650 inserts a reference into the credibility report where thereference is a URL, hyperlink, or other network identifier usable toobtain desired information from the data source partner over a network(i.e., Internet). For example, the reference may include a URL thatlinks to a map identifying the location of a business where the map ishosted by a third party mapping service. In some embodiments, thereference includes access parameters in addition to the reference inorder to obtain particular data from a third party or remote datasource. The access parameters may include registration or logininformation, data values, queries, or inputs to be used with thereference when obtaining information from the third party or remote datasource. In the mapping example above, the access parameters may includea street address that is passed as part of the URL query string to thethird party mapping service. The access parameters may further includescripts (e.g., JavaScript) that are executed when the remote data sourceis contacted or when the data is retrieved from the third party orremote data source.

In some embodiments, the credibility report is hierarchicallystructured. In this manner, access to the credibility report data can berestricted based on access rights associated with each hierarchicallevel. Data grouped to a first hierarchical level may be accessible byall parties and may include summary or high level information. Datagrouped to a second hierarchical level or lower hierarchical level mayinclude more restrictive access rights to allow the system operator tomonetize the presentation of this data through the interface portal.Access restrictions may be specified by the credibility system operator(i.e., interface portal operator), the entity to which the reportrelates, or by a set of defined access restriction rules that enumeratewhat credibility data is assigned to what access restriction level. Forexample, access restriction rules may be defined whereby the overallcredibility score for each entity is assigned a first access restrictionlevel that is accessible by all users and component scores (e.g., areview credibility score and a rating credibility score) are assigned asecond access restriction level that is accessible by entities or usersthat have paid for greater levels of access.

In some embodiments, the report generator 650 provides variousinteractive tools at different hierarchical levels of the credibilityreport to allow users the ability to edit, reorganize, or otherwisemanage the data that is grouped at that hierarchical level. The toolsmay include (1) a graphical element for presentation by the interfaceportal and interaction with at least a graphical pointing tool orkeyboard and (2) an Application Programming Interface (API) functioncall, sub-routine, script, or system call that implements the toolfunctionality.

Identification information such as the entity name, unique entityidentifier, address, etc. is also stored to the credibility report. Thisinformation is included so that different query strings and search termscan be used to locate and identify the appropriate credibility report.

Once a credibility report is stored to the database 220, thatcredibility report can be updated by the report generator 650 as newcredibility data becomes available. This may include changingcredibility or other data that was previously stored to the credibilityreport data structure or changing previously stored references or accessparameters that are used to obtain data from other data sources. In someembodiments, the report generator 650 may generate the credibilityreports before they are accessed by an individual or business or thereport generator 650 may generate the credibility reports on-demand asthey are requested by individuals or businesses.

The credibility reports include sufficient data which when presentedthrough the interface portal provide complete transparency into how acredibility score is derived. By providing different presentations ofthe credibility report according to the hierarchical levels, differentgroupings of data, and different access restrictions, each credibilityreport can be monetized differently. More specifically, each credibilitydimension and each hierarchical level of credibility data within eachcredibility dimension can be converted into a tangible asset whose datacan be separately monetized or freely distributed on a per user, perentity, or per access restriction basis. Users or entities access thecredibility reports through the interface portal 240, though someembodiments present the data in the credibility reports using othermediums such as in writing or by telephone consultation.

FIG. 12 illustrates a credibility report window 1210 within theinterface portal 240 in accordance with some embodiments. As shown, thecredibility report window 1210 includes multiple viewing panes 1220,1230, 1240, and 1250 with various information and actions therein.

Pane 1220 is the scores pane that presents the credibility score and/orcomponents of the credibility score such as a credibility ranking scoreand a credibility review score. In some embodiments, the credibilityscore identifies the overall credibility of the entity, while theranking score is derived from normalized quantitative measures ofquantitative data and the review score is derived from quantitativemeasures obtained from processing qualitative data. In some embodiments,the scores are presented using indicator bars and/or numerical values.The indicator bars may be color coded to better differentiate thescores. For example, a red color indicates a poor score, a yellow colorindicates a neutral score, and a green color indicates a good score.Also included within pane 1220 is button 1225. When the button 1225 isclicked, the report provides various suggestions as to how the entitycan improve upon the score, areas that need improvement, or areas thatare currently successful. Such information can be presented in a pop-updialog box or by changing the contents of the pane 1220.

Pane 1230 is the data editing pane. In this pane, users can eitheradjust a review that was aggregated from a data source or provide newdata that previously was not incorporated into the credibility score.This can include correcting errors in the aggregated data. Included inpane 1230 are buttons 1260 and 1265. Button 1260 allows for a specificentry within the pane 1230 to be expanded for editing. Button 1265allows a user to submit new credibility data including data that is notavailable at the various aggregated data sources or new data that hasnot yet propagated to the data sources.

Pane 1240 is the data matching pane whereby user reviews and otheraggregated credibility data can be viewed and mismatched data can beidentified and reported. Specifically, the entity can scroll through alist of aggregated quantitative and qualitative data to see what othersare saying about the entity. This includes viewing positive and negativefeedback, suggestions for improving the entity's credibility, issuesexperienced by users, what users like about the entity, etc.Additionally, the pane 1240 includes buttons 1270 and 1275 for expandinga specific entry and for reporting an error. The error may include datathat pertains to another entity and that was improperly matched to theentity for which the credibility report is generated. The error may alsoinclude data that should have been filtered out as biased data or as ananomaly. The pane 1240 may also present identification information aboutthe entity, such as addresses, agents, phone numbers, etc.

Pane 1250 is the customer service pane. In some embodiments, this paneprovides summary information about the credibility score and report suchas what the entity is doing well and what areas need improvement. Thispane can also provide suggested actions for the entity as well contactinformation for users seeking additional support. In some embodiments,the pane 1250 provides an interactive chat window to a customer supportrepresentative.

FIG. 13 presents an alternative credibility report viewer 1310 inaccordance with some embodiments. The credibility report viewer 1310provides a drill-down view for the credibility report whereby a user canobtain more detailed information about the credibility of an entity ateach drill-down layer. The credibility report viewer 1310 is displayedwith a first layer 1315 that provides a cumulative credibility score1320 for the entity. The cumulative credibility score 1320 is a singlenumerical or alphanumeric value that quantifies the credibility of anentity into a standardized score.

The user can click on the credibility score 1320 to drill-down to asecond layer 1330. When the user clicks on the credibility score 1320,some embodiments change the display of the credibility report viewer1310 from displaying contents of the first drill-down layer 1315 todisplaying contents of the second drill-down layer 1330. Navigationfunctionality allows a user to return back to the first drill-down layer1315 or any other layer at any time. Instead of changing the display ofthe credibility report viewer 1310, some embodiments provide a secondwindow or display area to display the second drill down layer 1330.

The second drill-down layer 1330 presents various component scores fromwhich the credibility score 1320 is derived. In some embodiments, thecomponent scores include a first score 1335, a second score 1340, and athird score 1345. In some embodiments, the first score 1335 is a scorethat quantifies the credit worthiness of the entity. The first score1335 may therefore be a Dun and Bradstreet credit score or other similarbusiness credit score. In some embodiments, the second score 1340 is arating score that quantifies the quantitative data that was aggregatedfrom the various data sources into a single score. In some embodiments,the third score 1345 is a review score that quantifies the qualitativedata that was aggregated from the various data sources into a singlescore.

The user can drill-down further to view the data that was used to deriveeach of the component scores. Specifically, by clicking on the firstscore 1335, the user drills-down to a third layer 1350 that presents acredit report. Alternatively, the user may be presented with a requestwindow from which the user can purchase a credit report. By clicking onthe second score 1340, the user drills-down to a third layer 1360 thatpresents the various aggregated quantitative data used in deriving therating score component of the credibility score 1320. Similarly, byclicking on the third score 1345, the user drills-down to a third layer1370 that presents the various aggregated qualitative data used inderiving the review score component of the credibility score 1320.

The user can click on any credit data, quantitative credibility data, orqualitative credibility data that is presented within the various thirddrill-down layers 1350-1370 in order to access another drill-down layer,such as layer 1380, that allows for users to correct errors andmismatched data, provide new data, or receive suggestions on how toimprove upon the various credibility score components. Suggestions maybe provided through another drill-down layer that provides aninteractive chat window that connects to a credibility specialist or byproviding guides on improving the various credibility score components.It should be apparent to one of ordinary skill in the art that anynumber of drill-down layers may be provided and that each layer mayinclude additional or other information than those presented in FIG. 13.

III. Indexer

The credibility scoring and reporting system is a configurable constructthat can be enhanced with one or more components that compliment theabove described scoring and reporting functionality. FIG. 14 illustratesthe credibility scoring and reporting system enhanced with one suchcomponent, indexer 1410. The indexer 1410 is integrated into thecredibility scoring and reporting system as one or more software modulesthat execute on the same or different hardware as the master datamanager 210, database 220, reporting engine 230, and interface portal240. The indexer 1410, in conjunction with or independent of thecomponents 210, 220, 230, and 240, acts to transform general purposecomputer or electronic hardware to a specific purpose machine thatutilizes the aggregated credibility data and derived credibility scoresto produce various tangible assets that provide further insight into thecredibility of an entity. Some such assets include comparative indices,automated credibility forecasts based on index trends, automatedidentification of successful and unsuccessful practices, predictivecredibility contribution from various practices, automated partner leadgeneration, predictive credibility contribution from variouspartnerships, preliminary credibility scores, event adjusted credibilityscores, and passive credibility monitoring tools.

A. Indices for Comparative Analysis

i. Credibility Score Indices

In some embodiments, the tangible assets produced by the indexer 1410include credibility score indices. An index of the produced set ofindices presents an aggregate set of credibility scores of variousentities that are selected according to one or more credibilitydimensions specified for that index. Each index serves to comparativelyconvey the credibility score of one entity in relation to thecredibility scores of other entities that are identified based on theone or more dimensions of credibility specified for that index. Eachindex therefore presents the credibility score for a particular entityin a comparative light that would otherwise be unavailable when viewingthe credibility score for a particular entity in isolation. In otherwords, one can determine from an index, how the credibility of aparticular entity compares to its competitors and other entities thatare related based on the dimensions of credibility specified for thatindex. These comparisons can be drawn because the credibility scorespresented in each index are uniformly derived using the same algorithmsthat eliminate individual biases. Also, these comparisons can be drawnbecause the credibility scores presented in each index are derived usingcredibility data that is aggregated from multiple data sources therebyeliminating the potential for credibility scores that do notholistically account for the full credibility of the various entitiesrepresented in the indices.

By adjusting the credibility dimensions for a presented index, one candetermine how the credibility of a particular entity compares todifferent sets of competitors and other entities. This comparativeinformation is valuable market research and has revenue generationimplications, because the information can be used to identify (1) marketposition of the particular entity, (2) market position of competitors orrelated entities, (3) relative success of the particular entity'spractices, and (4) growth of the particular entity.

Throughout this disclosure, an index will be described as an aggregateset of credibility scores. However, this definition is intended forexemplary and simplification purposes and is not intended to belimiting. It should therefore be apparent to one of ordinary skill inthe art that an index can be formed based on an aggregate set of anycredibility data that is included within the credibility reports of someembodiments. For example, the indexer 1410 may generate indices based onaggregated quantitative credibility data (e.g., aggregated rankings)instead of the aforementioned credibility scores.

In some embodiments, the indices are commoditized and monetizedseparately from the credibility scores and credibility reports. In otherwords, each index or a set of indices related to an entity can bebundled and provided or sold separately from the credibility score andreport generated for that entity. In some other embodiments, one or moreindices that are related to a particular entity are integrated into thecredibility report for that particular entity and provided or sold asone asset.

FIG. 15 presents a process 1500 performed by the indexer 1410 togenerate an index in accordance with some embodiments. The process 1500begins by obtaining (at 1510) one or more dimensions of credibility onwhich credibility scores for an index are to be aggregated. Some suchdimensions include geographic location, classifications andsub-classifications for the type of business, time, demographicinformation, and information relating to provided goods or services. Forexample, an index may be generated to include credibility scores forentities that are located in the 90000 zip code, that operate asrestaurants, that operate as restaurants primarily serving Frenchcuisine, and that cater to high-end clientele (i.e., high-priced goodsand services). These dimensions do not provide a complete listing ofdimensions across which indices can be derived. Other dimensions ofcredibility or other combinations of dimensions can be used to compiledifferent sets of credibility scores associated with different sets ofentities for an index. In some embodiments, the indexer obtains thesedimensions from a system administrator, from a user accessing theinterface portal, or based on a set of predefined dimensions that areused to generate a default set of indices for each entity for whichcredibility data has been aggregated. The default set of indices can beappended to with new indices at any time by users, entities, or systemadministrators specifying different dimensions or different combinationsof dimensions for the new set of indices.

Based on the obtained dimensions, the process performs (at 1515) one ormore queries to the database of the credibility scoring and reportingsystem (i.e., database 220). The queries retrieve credibility scores forentities that satisfy the obtained dimensions. As shown in FIG. 5 above,the database stores the credibility scores and reports associated withthe entities in data structures that also store credibility data andidentification information relevant to searching and identifyingentities based on various credibility dimensions. The process aggregates(at 1520) the credibility scores for those identified entities.

The process generates (at 1530) an index using the aggregatedcredibility scores. In some embodiments, generating an index involvescomputing statistical information related to the aggregated credibilityscores. Such statistical information includes computing a mean, median,standard deviation, percentages, z-score, and distribution for theaggregated credibility scores. Generating an index also involves,compiling the aggregated credibility scores and the computed statisticalinformation into a representation. The representation may include agraphical representation such as a chart, graph, or other visual means.The graphical representation may demarcate a particular business ofinterest in the index with a graphical indicator so that the credibilityscore of that particular entity can be easily and readily identified inthe index and so that the index can be used to easily and readilycomparatively analyze the credibility score of that particular entity tothe credibility scores other entities identified in the index. This isreferred to as “keying” an index to a particular entity. Additionally,the graphical representation can be made interactive so that a user orentity can move a pointing tool along the graphical representation toidentify different entities at different positions along the graphicalrepresentation of the index.

The process links (at 1540) the generated index to each entity whosecredibility score or other credibility data is used in the compilationof the index Linking may include associating the index to the datastructures in the database for the corresponding entity. The index isthen stored (at 1550) to the database of the credibility scoring andreporting system and the process ends. In some embodiments, storingreplicates the index in the data structure of each corresponding entity.In some other embodiments, a single instance of the index is stored tothe database and the data structure of each entity associated with thatindex is updated with a link to the stored index. Accordingly, eachgenerated index may have a one-to-many relationship wherein an index islinked to several entities. Each generated index may also have amany-to-one relationship wherein several indices can be linked to aparticular entity.

Once stored to the database, the index is available for subsequentpresentation. A user or entity can submit a query to retrieve andpresent the index using the interface portal. The search can beconducted by specifying credibility dimensions for a desired index. Thesearch can also be conducted by specifying the name of an entity orother identifier in order to retrieve indices related to that entity.Also, a search can include some combination of dimensions and entityidentification information to target a particular index or set ofindices. For example, a user can specify a query using the interfaceportal to view an index related to entities in the state of Nevada thatare engaged in the sale of furniture. Alternatively, the user canspecify a query to view one or more indices associated with an entityhaving a name of “Acme Furniture” in the state of Nevada.

FIG. 16 presents a set of indices (1610, 1620, 1630, 1640, 1650, and1660) that are linked to a particular entity in accordance with someembodiments. The credibility score of the particular entity is shown at1670 and is demarcated in each of the indices using a graphicalindicator (e.g., 1685). Each index in the set of indices 1610-1660conveys the credibility score of the particular entity according todifferent dimensions. As shown, the different dimensions includegeographic location, classification and sub-classification of theparticular entity, and time. The different dimensions comparativelyidentify the credibility score of the particular entity in relation tothe credibility scores of different entities that satisfy the differentdimensions that are associated with each index in the set of indices1610-1660.

In FIG. 16, the credibility score for the particular entity isillustrated as a numeric value of 550 at 1670 and the particular entityis a restaurant. Index 1610 comparatively illustrates how thecredibility score of 550 relates to the credibility scores otherrestaurants where the dimension of credibility associated with index1610 is a single dimension that focuses the index to identify andinclude all entities that operate as restaurants. In index 1610, agraphical representation in the form of linear range 1680 with ademarcation indicator 1685, percentage value 1690, and distributiongraph 1695 convey how the credibility score of 550 for the particularentity compares with the credibility scores of all other restaurantsthat are associated with the index 1610. The demarcation indicator 1685identifies the credibility for the particular entity. As shown, thecredibility score of 550 places the particular entity in the 80^(th)percentile of all restaurants. This information may be important to theparticular entity in order to understand how successful this particularentity is relative to other restaurants. Moreover, the particular entitycan appreciate the overall state of the restaurant business andunderstand how satisfied clients are with the services of the particularentity relative to other restaurants.

In some embodiments, the index 1610 is interactive such that users canplace a pointing tool (i.e., mouse cursor) over various parts of thelinear range 1680 in order to obtain callouts that identify who are theother restaurants with credibility scores that exceed or that are lowerthan the 550 credibility score of the particular entity. For example,FIG. 17 illustrates a zoomed-in view of an index 1710 that presents aplotted distribution of all entities that satisfy the dimension of theindex 1710. When the user places the pointing tool 1720 over a plottedpoint, a callout box 1730 is presented to identify the entity that isassociated with that plotted point and other information pertaining tothe credibility of that entity, such as its credibility score. Otherinteractions, such as clicking on a plotted point, can be used to drilldown into the credibility score or credibility report that is associatedwith that plotted point. In this manner, users or entities can quicklyand interactively drill down from the index 1710 to access more detailedinformation for understanding the derivation of a particular credibilityscore presented in the index 1710. This allows for lower levelcomparative analysis whereby users and entities can go beyondcomparative analysis of the credibility scores presented in the index1710 to perform comparative analysis of credibility data used in thederivation of the credibility scores. In so doing, users and entitiescan specifically identify what are the significant contributing factorsto the credibility scores of more or less successful entities. Users andentities can then adjust their own strategies and practices to integratethe positive contributing factors and remove the negative contributingfactors in order to improve their credibility score and, as a result,improve their revenue.

With reference back to FIG. 16, index 1620 illustrates the credibilityof the particular entity relative to different dimensions of credibilitythan that of index 1610. Specifically, index 1620 comparativelyillustrates how the credibility score of 550 for the particular entityrelates to the credibility scores of other entities operating asrestaurants in a defined geographic region, the state of California.Accordingly, the set of entities used to compile index 1620 will besmaller than the set of entities used to compile index 1610, becauseonly restaurants that are in California are used to compile index 1620whereas index 1610 is compiled using all restaurants irrespective ofgeographic region. By comparing index 1610 with index 1620, it can beseen that the restaurant business is more competitive or subject tohigher credibility in California as the particular entity ranks at the65^(th) percentile when the credibility score of 550 is compared to thecredibility scores of other restaurants in California; the samecredibility score of 550 ranks at the 80^(th) percentile when comparedto all restaurants.

Other indices presented in FIG. 16 provide other comparative views forthe credibility score of the particular entity according to differentdimensions. For example, index 1630 shows that the credibility score ofthe particular entity ranks in the top 10% when the geographic region isfurther restricted to the city of Malibu, Calif. From indices 1610-1630,the particular entity may determine that it has historically performedbetter than a majority of its competitors. Each credibility dimension ofthe different indices may reveal additional information that is unknownto the particular entity. For example, index 1660 comparativelyillustrates how the credibility score of the particular entity hasdeviated (i.e., risen or declined) in the last six months relative toits competition. As shown in index 1660, the particular entity ranks inthe 20^(th) percentile. This indicates that in the past six months, thecredibility of the entity has performed poorly. This can be an earlyindicator of the public's perception as to the performance or quality ofthe particular entity and of a potential decline in future revenue.Accordingly, the particular entity can utilize the index 1660 toidentify and address these issues before they increase to impact the“bottom-line” (e.g., revenue or profitability) of the particular entity.

In some embodiments, the indexer provides other interactive tools thatare associated with the indices besides those described with referenceto FIG. 17. These tools allow users the ability to interact with thepresented data in order to obtain more detailed information andon-the-fly adjust one or more dimensions of credibility associated witha displayed index. These tools are presented as interactive userinterface elements in a web browser or other application with networkaccess. In some embodiments, the interface portal performs theformatting for displaying an index in a web browser application with anycorresponding interactive tools. FIG. 18 illustrates two interactivesliders 1810 and 1820 associated with an index that is “keyed” to aparticular entity. When an index is keyed to a particular entity, theinteractive tools 1810 and 1820 are used to change the dimensions of theindex relative to the particular entity.

The first slider 1810 is an interactive tool for changing the geographicdimension of the index. Since the index is keyed to the particularentity, adjustments made to the slider 1810 alter the geographicdimension of the index according to geographic data associated with theparticular entity. For example, the geographic data associated with afirst entity includes the state of Florida, the city of Miami, and thezip code 33133 and the geographic data associated with a second entityincludes the state of California, the city of Malibu, and the zip code90265. When the slider 1810 is keyed to the first entity, adjustments tothe position of the slider 1810 can be used to on-the-fly alter thegeographic region associated with the index from all entities, toentities in the state of Florida, to entities in the city of Miami, andto entities in the zip code 33133. Similarly, when the slider 1810 iskeyed to the second entity, adjustments to the position of the slider1810 can be used to on-the-fly alter the geographic region associatedwith the index from all entities, to entities in the state ofCalifornia, to entities in the city of Malibu, and to entities in thezip code 90265. In some embodiments, a user can on-the-fly change whichentity an index is keyed to by selecting a different entity in theindex. The selection can be made using a click action on the plottedpoint in the index that represents that different entity.

The interactive tools (e.g., sliders 1810 and 1820) that are presentedin conjunction with the same index will be keyed to the same particularentity. Accordingly, the slider 1820 will be keyed to the sameparticular entity as the slider 1810. However, the slider 1820 can beused to on-the-fly adjust a different dimension of the index than slider1810. In this figure, slider 1820 adjusts the granularity of theindustry that is presented by the index. Since the slider 1820 is keyedto the particular entity, the slider 1820 adjusts the granularityaccording to different industry classifications that are associated withthe particular entity. In this figure, the different industryclassifications that are associated with the particular entity include arestaurant classification at a first classification level, a Frenchrestaurant classification at a second classification level, and a finedining French restaurant classification at a third classification level.Accordingly when the slider 1820 is at a first setting, the index ofFIG. 18 changes to display credibility scores for the particular entityin relation to all other entities; when the slider 1820 is at a secondsetting, the index of FIG. 18 changes to display credibility scores forthe particular entity in relation to all other restaurants; when theslider 1820 is at a third setting, the index of FIG. 18 changes todisplay credibility scores for the particular entity in relation to allother French restaurants; and when the slider 1820 is at a fourthsetting, the index of FIG. 18 changes to display credibility scores forthe particular entity in relation to all other fine dining Frenchrestaurants. It should be noted that the slider 1820 can be used inconjunction with the slider 1810 or any other slider to producedifferent permutations for the index where each permutation isassociated with a different combination of credibility dimensions.

In summary, different indices allow the particular entity to targetparticular dimensions of credibility that are of interest to theparticular entity and to see how the entity compares to other entitiesthat satisfy the same dimensions of credibility. This allows theparticular entity to form a comprehensive view of its credibility in acomparative and relative manner as opposed to an isolated andindependent manner that would otherwise be obtained when viewing thecredibility score of the particular entity without any frame ofreference as to the credibility scores of other entities or competitorsthat meet similar credibility dimensions. Moreover, the interactivetools allow for on-the-fly adjustments to be made to the indices tochange the dimensions, to change the entity to which the index is keyed,and to provide drill-down functionality in order to expand thecredibility scores in the index into their respective credibility datacomponents.

While FIG. 16 illustrates geographic, classification, and temporaldimensions of credibility, these dimensions are not intended to berestrictive or comprehensive. Rather, the indexer of some embodimentscan generate indices on various other dimensions or differentcombinations of dimensions based on searchable fields related to data ofthe data structures, credibility reports, or other data associated withthe credibility scores of the entities that are stored to the databaseof the credibility scoring and reporting system.

In some embodiments, the indices are tangible assets that are freelyaccessible or are available on a pay-per-access or subscription model.In this manner, the credibility scoring and reporting system cancommoditize and monetize the indices. In some embodiments, the freelyaccessible indices include a predefined first set of indices that, forexample, comparatively present credibility of an entity at a fixed setof one or more dimensions. Should the user desire to adjust thedimensions to access different indices or to specify queries for indicesbased on custom dimensions, the user could pay a fee or signup for asubscription plan in order to obtain access. In some embodiments, usersmay be provided free or unrestricted access to an index pertaining to afirst dimension of credibility and access to a second or somecombination of second dimensions is restricted to paying users. In stillsome other embodiments, access to all indices may be restricted topay-per-access.

Each index derives its value from the ability to provide comparativeinsight as to an entity's competition and to guide future strategy ofthe entity by enabling the entity to more directly compete with itscompetition through identification of the competition and throughidentification of the credibility data associated with the competition.From this targeted credibility data, the entity can ascertain what thepublic positively and negatively perceives about each competing entityso that the entity can streamline its future strategies accordingly. Assuch, the indices are valuable assets that, when used, can improve therevenue generation capabilities of an entity.

ii. Credit Based Indices

The above processes, dimensions, and interactive tools used in thegeneration and presentation of the credibility scoring indices may beadapted to generate indices that are based on credit data orcreditworthiness of an entity. Such credit data is part of the data thatis aggregated and stored to the credibility scoring and reportingdatabase by the master data manager. More specifically, the credit datais aggregated based on established partnerships with credit datasources, such as Dun & Bradstreet, TransUnion, and Equifax as someexamples. In some embodiments, the master data manager is providedaccess to the compiled credit data from each such credit data source.

The indexer leverages the aggregated credit data to generateconfigurable dimensional credit indices. These credit indicescomparatively present the creditworthiness of a particular entity inrelation to other entities that are identified by the one or moredimensions that are defined for each credit index. In so doing, thetraditional isolated view of a credit score is replaced with a moremeaningful comparative view that allows entities to better appreciatetheir creditworthiness. In turn, these credit indices promote betterlending practices by banks and better business evaluation by analystsbecause of the comparative light in which the credit scores arepresented. For example, the credit score for a particular business whencompared against other businesses nationally may be an average creditscore, but when that same credit score is compared to other businessesthat operate in the same geographic region as the particular business,it may be that the credit score is very good for that region. A regionalbank using the credit indices that are generated for the particularbusiness can then safely extend more credit to the particular businessthan it may otherwise have been willing to do if the regional bank wasonly privy to the credit score of the particular business without beingable to compare the credit score to the credit scores of otherbusinesses in the same geographic region.

In some embodiments, the indexer generates the credit indices in thesame customizable manner with which the credibility indices describedabove are generated. Specifically, the indexer generates the creditindices by aggregating credit scores for different entities that satisfyone or more defined dimensions for the indices. In some embodiments, thedata from the different credit indices that are keyed to a particularentity and that are generated according to different dimensions iscondensed to produce different credit ratings for that particular entitywhere the credit ratings are different than the credit scores derived byvarious credit reporting bureaus. For example, a first credit rating maybe generated for a particular entity to quantify the credit for thatparticular entity relative to other entities operating in a particulargeographic region and a second credit rating may be generated toquantify the credit for that particular entity relative to otherentities that operate in the same field of business. In this manner, theindexer provides a holistic view for the creditworthiness of theparticular entity. In some embodiments, the indexer groups the creditratings and the credit score(s) for a particular entity andhierarchically structures the grouped credit ratings and the creditscore(s) for hierarchical presentation of the creditworthiness for thatparticular entity.

As noted above, the indexer generates a credit index in a similar mannerto generating a credibility score index. To generate a credit index, theindexer first obtains one or more dimensions that define the scope ofthe credit index. Some different dimensions for a credit index includethose dimensions and other dimensions described above with reference tocredibility score indices. Some such dimensions include geographicregion (e.g., state, city, zipcode, etc.), industry (based on standardindustrial classification (SIC) codes), sub-industry, temporaldelimiters (i.e., years in business), size of the company, etc. Thesedimensions may be specified by a user using the interface portal, by asystem administrator, or by a set of predefined rules for creating thecredit index.

The indexer queries the database to retrieve credit scores for entitiesthat satisfy the obtained dimensions and the retrieved credit scores areorganized according to a preferred distribution in order to generate thecredit index. In some embodiments, the retrieved credit scores includeone or more of a Paydex score, a financial stress score, a commercialcredit score, and a supplier evaluation risk score.

FIG. 19 illustrates a plotted distribution of credit scores that isillustrative of a credit index 1910 in accordance with some embodiments.In this figure, each plotted point of the distribution represents acredit score of an entity that satisfies the one or more dimensionsspecified for the credit index 1910. When the credit index 1910 is keyedto a particular entity, the plotted point representative of the creditscore for that particular entity is shown with a special symbol orindicator, such as indicator 1920. In some embodiments, each plottedpoint of the credit index 1910 is interactive such that the user canposition a pointing tool, such as mouse cursor 1930, over a plottedpoint and, in response, the name of the entity and the credit score ofthe entity that is represented by that plotted point is displayed (e.g.,1940). Further interactions allow users to drill-down into the creditscore. For example, a mouse left-click action on a particular plottedpoint drills-down from the credit score into one or more credit ratiosfor the represented entity. It should be apparent to one of ordinaryskill in the art, that a credit index may be generated according tovarious other distributions such as linear charts, bar charts, piecharts, etc.

Credit indices may be generated as requested or may be pre-generated andstored to the system database for subsequent presentation. In someembodiments, each credit index is used to derive one or more creditratings. A credit rating represents how the credit score of a particularentity in a particular credit index compares to the credit scores ofother entities in that particular credit index. In other words, thecredit rating represents how the credit score of the particular entitycompares to the credit scores of other entities that satisfy the sameone or more dimensions. In some embodiments, the credit rating is basedon an “A”, “B”, “C”, “D”, and “F” scale. However, any scale may be usedto represent the credit rating without loss of the intended purpose.Multiple ratings may be derived and presented at one time in a report ordisplay interface. Additionally, multiple ratings derived for aparticular entity may be hierarchically organized to provide drill-downaccess to the ratings.

FIG. 20 illustrates using drill-down functionality to hierarchicallyaccess credit ratings of a particular entity in accordance with someembodiments. Specifically, the figure illustrates credit score 2010 at afirst hierarchical level, single dimensional credit ratings 2020, 2030,and 2040 at a second hierarchical level, and a third hierarchical level2045 to specify a multi-dimensional credit rating. The credit ratings2020-2045 are derived based on comparative analysis of the credit score2010 to credit scores of other entities that are identified according toone or more different dimensions that are specified for each creditrating. Each credit rating 2020-2045 also includes an interactive tool(e.g., 2050, 2060, 2070) for adjusting the dimension for that creditrating.

When the credit score 2010 is presented through the interface portal,the user can click or otherwise interact with the credit score 2010 toexpand the credit score and display the credit ratings 2020-2040. Inthis figure, credit rating 2020 presents how the credit score 2010 for aparticular entity rates when compared against the credit scores forother entities in the same SIC code as the particular entity; creditrating 2030 presents how the credit score 2010 for the particular entityrates when compared against the credit scores for other entities in thesame zipcode as the particular entity; and credit rating 2040 presentshow the credit score 2010 for the particular entity rates when comparedagainst the credit scores for other entities that have been operatingfor less than 3 years. Each credit rating 2020-2040 therefore providesdifferent insight into the creditworthiness of the particular entity.For example, when viewed in isolation, one may interpret the creditscore 2010 to be a low score that is representative of an entity that isnot creditworthy. However, the credit ratings 2020-2040 reveal that theentity when compared to other entities in its industry, geographicregion, and years of business is actually creditworthy.

In this figure, the interactive tool 2050 above the credit rating 2020can be used to broaden or restrict the SIC code. For example, moving theslider further down accesses a sub-classification for the industry thatthe particular entity is in and, in so doing, the credit score 2010 ofthe particular entity will be compared to the credit scores for a subsetof entities that are within the same sub-classification in order tocompute a new rating. The interactive tool 2060 allows users tointeractively change the geographic region used to determine the creditrating 2030 and the interactive tools 2070 is an insertion box where theuser can type in the temporal dimension for determining the creditrating 2040.

A credit rating may be determined based on multiple differentdimensions. To do so, a user can click on any of the credit ratingrepresentations and then set one or more other dimensions to furtherrefine the credit rating. In FIG. 20, the user clicks on the creditrating 2040 graphical representation to access the third hierarchicallevel 2045 whereby the temporal dimension can be combined with one otherdimension to produce multi-dimensional credit rating. As shown, thethird hierarchical level 2045 presents two multi-dimensional creditratings. A first multi-dimensional credit rating is determined based onthe number of years in business and a SIC code and a secondmulti-dimensional credit rating is determined based on the number ofyears in business and a geographic region.

In some embodiments, right-clicking on a credit rating (e.g., 2020-2040)causes the associated credit index to be displayed on-screen. The layoutand operation of FIG. 20 is not meant to be limiting and is provided forexemplary purposes. Accordingly, the credit ratings, credit indices, andinteractive tools can be presented in any number of different ways.

Some embodiments monetize the credit information by restricting accessto the credit ratings or credit indices to paying users. In someembodiments, users are provided access to the credit score information,but have to pay or subscribe in order to access the associated creditratings or credit indices. Access to the credit ratings and creditindices is often essential to lenders and other consumers of creditinformation, because the same credit score for a business operating inManhattan, N.Y. and for a business operating in Lincoln, Nebr. does notproperly describe the creditworthiness of these two businesses withoutfurther context. Each state, and more specifically, each city is subjectto its own micro-economic influences that cause credit scores in thatcity or state to be offset from credit scores in other cities or states.These micro-economic influences are more likely to impact thecreditworthiness of the small business as opposed to large interstatecorporations where the creditworthiness of the small business is whollydependent on its operations within a limited geographic region.Therefore, the credit ratings and credit indices generated by thecredibility scoring and reporting system of some embodiments is avaluable asset for properly determining the creditworthiness of anentity.

B. Credibility Trends

In some embodiments, the indexer leverages the aggregate data in thecredibility scoring and reporting system to generate other tangibleassets. Some such assets include credibility trends. Credibility trendsimpart additional insight into entity credibility by forecastingfluctuations and direction of entity credibility based on current andhistorical credibility data related to the entity, current andhistorical credibility data related to other entities that areidentified according to one or more dimensions, and trending factorsthat are often outside the immediate influence of the entity. As such,the credibility trends are valuable commodities in identifying futureissues to credibility and in enabling an entity to preemptively addresssuch issues. Based on their informative value, credibility trends can bemonetized as a sellable asset apart or in conjunction with thecredibility scores, credibility reports, and indices.

In some embodiments, the indexer derives a credibility trend based onanalysis of current and historical credibility data of a particularentity in relation to aggregate credibility data from one or moreindices that are associated with that particular entity. The credibilityscoring and reporting system stores historical credibility data to thesystem database. Specifically, as credibility scores, credibilityreports, and indices are updated over time, the credibility scoring andreporting system stores snapshots of this data in the database such thata historic account of the particular entity's credibility and itsassociated indices are available. Snapshots may be taken on a periodicbasis, such as on a daily, weekly, or monthly basis.

FIG. 21 presents a process 2100 performed by the indexer in order toidentify a trend for a particular entity in accordance with someembodiments. The process 2100 begins by retrieving (at 2105) current andhistoric snapshots of at least one index that is associated with theparticular entity. The at least one index is retrieved using a queryfrom the indexer to the database wherein the query identifies (1) theparticular entity, (2) a desired set of credibility dimensions for theat least one index, and (3) a timeframe for the historic snapshots ofthe index or indices that satisfy the desired set of credibilitydimensions. The query parameters may be set by the system or by a user.

Upon retrieving the snapshots for a query identified index, the processcompares (at 2110) the credibility of the particular entity relative tothe credibility of other entities identified in the index over thespecified timeframe. This comparison is performed using one or morealgorithms that identify dimensional and temporal trends between thecredibility of the particular entity and the credibility of otherentities in the index over the specified timeframe. These algorithmsdefine the comparisons that are made for trend identification and thethresholds for identifying similar or dissimilar patterns or behavior astrends. For example, identifying a trend may include identifying thatthe credibility score of a particular entity increased by 10% over a onemonth interval and the credibility scores of other entities had anoverall decrease of 10% over the same one month interval. Thiscomparison reveals a trend that shows the particular entityoutperforming other related entities. An alternate example of trendidentification is provided below with reference to FIG. 22. Trends canbe identified based on any one or more dimensions of credibility andbased on different credibility data besides credibility scores.Moreover, trends can be identified across different dimensions such thatobserved behavior in a first credibility dimension has a correspondingimpact on a second dimension of credibility. For example, an increase insocial network messages referencing a particular entity can correspondto an increased credibility score as the increase in social networkmessages may be indicative of increasing popularity or exposure.

Next, the identified trends are adjusted (at 2120) based on zero or moretrending factors. Trending factors include influences that impactcredibility of an entity and that are indirect or outside the immediateinfluence of the particular entity. Trending factors are typically notobservable from an index. One trending factor involves determining howcrowded the market is in which the particular entity operates andadjusting the trends accordingly. For example, when a large number ofentities go out of business in an observed time period, this may be anindication that the market is over-saturated and, as a result, theindexer adjusts any compiled trends to account for the adverse state ofthe market. Conversely, when a large number of entities newly enter themarket, this may be an indication that there is high demand and, as aresult, the indexer adjusts any compiled trends to account for thepositive state of the market. Another factor may include determining how“hot” the market that the particular entity operates in is and adjustingthe trends accordingly. For example, from the quantity of credibilitydata that is aggregated for the particular entity relative to thequantity of credibility data that is aggregated for related entitiesidentified from the index over the specified time period, the indexercan determine whether the particular entity is outperforming itscompetition. If the indexer aggregates five total new positive reviewsin a one month period for a particular entity of interest and aggregatesone hundred total new positive reviews for an average competitor of theparticular entity of interest as identified from the query identifiedindex, then the indexer can ascertain that the particular entity isunderperforming and, as a result, that the credibility of the particularentity will be adversely affected. Other trending factors includeanalyst reports or news articles on the economy, a business sector, or afield of business. The above listing is exemplary in scope and notintended to be an exhaustive listing of all trending factors, as thecomplete set of factors is too numerous to enumerate and can change overtime. In some embodiments, trending factors are supplied to the indexerby a system administrator or via automated means.

A mathematical formula is derived (at 2130) from the adjusted trends andapplied (at 2140) to the credibility score of the particular entity toproduce a future model for how the credibility score is expected tochange based on the analysis of current and historic credibility data ofthe particular entity relative to its competition or other relatedentities. In some embodiments, trends and the forecasted modelassociated with the particular entity are presented through theinterface portal when requested by a user or the particular entity. Theadjusted trends and forecasted model can alternatively be stored to thedatabase for subsequent presentation to one or more users upon request.

FIG. 22 conceptually illustrates identifying a trend based oncomparative analysis between the credibility of a particular entity andthe credibility of other related entities that are identified from a setof indices that are generated to present the credibility of theparticular entity according to different dimensions. The set of indicesinclude index 2210, index 2220, and index 2230. Index 2210 illustratesthe credibility index for frozen treat businesses over a specifiedperiod of time. As shown, index 2210 is relatively stable over thespecified period of time. Index 2220 illustrates the credibility indexfor ice cream businesses over the same specified period of time wherethe ice cream businesses are a subset of businesses that are included aspart of the frozen treat businesses. Index 2220 is declining over thespecified period of time. Index 2230 illustrates the credibility indexfor frozen yogurt businesses over the same specified period of timewhere the frozen yogurt businesses are a subset of businesses that areincluded as part of the frozen treat businesses. Index 2230 isincreasing over the specified period of time. Index 2240 illustratescredibility of a particular frozen yogurt business as being stableduring the specified period of time. By aggregating the information fromthe indices 2210-2230, the indexer is able to identify a pattern inwhich aggregate credibility for frozen treat businesses stays relativelystable even though frozen yogurt businesses are gaining credibility andice cream businesses are losing credibility. Since the particularbusiness at issue is a frozen yogurt business, the indexer can forecasta trend 2250 whereby the credibility of the particular business willincrease. This trend would be unobtainable when viewing credibility ofthe particular business in isolation. However, by allowing the indexeraccess to this aggregate data, these and other trends are readilyidentifiable and presentable to users in an automated manner.

In some embodiments, the above trend information is converted into atangible asset that is sold as a commodity. In some such embodiments,such a commodity is made available to users that pay to have access tothe underlying information using the interface portal or through othermeans (e.g., paper reports). Other non-paying users will be preventedfrom accessing the trend information or will be provided limited orrestricted access. As will be apparent from the above description, theidentified trends have separate value from the credibility scores,credibility reports, and indices by virtue that the trends extend beyondthe past and present credibility outlook that is otherwise obtainablefrom the credibility scores, credibility reports, and indices. Instead,the identified trends provide an expected future credibility outlook toallow an entity to preemptively address expected changes to itscredibility before they occur and to allow an entity to determine whatfuture revenue may be expected based on expected changes to credibility.

C. Predictive Credibility

In some embodiments, the indexer creates tangible assets that can bemonetized in the form of reports (1) that identify successful practicesof an entity that improve upon its credibility, (2) that identifyunsuccessful practices of the entity that degrade its credibility, (3)that identify successful and unsuccessful practices of competitors orother related entities that improve or degrade credibility, and (4) thatpredict how a change, addition, or removal of a practice will affect thecredibility or credibility score of the entity in the future. Theseassets derive their value from the fact that they directly identify forthe entity what targeted actions can most effectively improve thecredibility of the entity where the actions correspond to practices thatshould be changed, implemented, or removed. Moreover, the predictivescoring identifies the impact that a specific action will likely producein the credibility score of the entity so that the entity can make acost-benefit analysis to determine how much revenue may be generated asa result of taking the action.

The indexer automatedly identifies the successful and unsuccessfulpractices using data that is aggregated as part of the indices that areassociated with an entity. A successful practice is representative ofany action that is identified by one or more producers of credibilitydata as being beneficial to the credibility of an entity and anunsuccessful practice is representative of any action that is identifiedby one or more producers of credibility data as being detrimental to thecredibility of an entity. The actions constituting successful andunsuccessful practices may include the entity's shipping policy (e.g.,free shipping, expedited shipping, refrigerated shipping, etc.), nohassle returns, upfront pricing, use of fresh ingredients, free parking,customer service, etc. These actions may also include whether or not anentity has a social media presence (e.g., periodically “tweets”, postson a social media site, etc.). The listing of actions is not intended tobe limiting, but is rather presented for exemplary purposes.

In some embodiments, the indexer automatically computes the predictedchange to the credibility score of an entity when the entity undertakesa new action or ceases a previously undertaken action. The predictedchange to the credibility score is based on the computed averagecredibility score contribution that a particular action has had on thecredibility scores of competitors and other related entities.

FIG. 23 presents a process 2300 performed by the indexer to identify fora particular entity the successful and unsuccessful practices of itscompetitors or of related entities. The process 2300 begins byidentifying (at 2305) the particular entity that is interested inidentifying what practices can help improve upon its credibility andwhat practices are currently implemented by the particular entity thatdetrimentally affect its credibility. The process selects (at 2310) atleast one index that is associated with the identified particularentity. The index is selected using the links that associate theparticular entity to one or more indices, though a query can be made tothe credibility scoring and reporting system to select an index for theparticular entity. In some embodiments, the process by default selects a“credibility score index” that identifies the credibility score of theidentified particular entity relative to the credibility scores of itscompetitors within a specified geographic region, where the specifiedgeographic region provides a sufficient sample size of competitors(e.g., 100 competitors).

Based on the entities identified in the selected index and the positionsof the entities in the index, the process groups (at 2320) some of theentities to at least one group. For exemplary purposes of process 2300,the discussion below is presented with respect to grouping some of theentities from the index into one particular group. In some embodiments,the grouping identifies entities ranking in the top percentile of theindex. From the credibility data that is associated with these topranking entities, the indexer can identify common practices that, ifimplemented by the particular entity, could positively impact thecredibility of the particular entity. Other groupings can identifyentities ranking in the bottom percentile of the index in a secondgroup. As should be apparent to one of ordinary skill in the art, thesegroupings are readily identifiable from the index, because the selectedindex orders the entities associated with that index according to ascore (e.g., credibility score). The process 2300 can alter how thegrouping is performed for different indices, a desired sampling size, ora percentile value for the group. For example, based on a first index,the process groups entities that rank in the top 10 percentile of thefirst index to a first group and groups entities that rank in the bottom10 percentile of the first index to a second group; based on a secondindex, the process groups entities that rank in the top 50 percentile ofthe second index to a first group and groups entities that rank in thebottom 50 percentile of the second index to a second group. It should benoted that the process 2300 can be adjusted so that any arbitrary numberof groups are identified at step 2320, though the discussion below ispresented with only a single group.

The process retrieves (at 2330) credibility data that was aggregated forthe entities that were grouped to the at least one group. The processutilizes the entity names or unique identifiers associated with theentities in the group to retrieve the associated credibility data fromthe database. The retrieved credibility data includes aggregatedqualitative credibility data and may include aggregated quantitativecredibility data and other data.

The process analyzes (at 2340) the retrieved credibility data toidentify commonality for the group. This involves natural languageprocessing of the retrieved credibility data, and more specificallynatural language processing of the retrieved qualitative credibilitydata. As part of the natural language processing, the indexer searchesthe retrieved qualitative credibility data to identify common terms orphrases that are repeated a sufficient number of times to satisfy acommonality threshold. More specifically, the indexer searches thecredibility data for specific terms or phrases related to differentaspects of an entity, different practices utilized by the entity, or togoods and services that are offered by the entity. The search may befacilitated by a dictionary that enumerates the terms or phrases thatare related to the different aspect, practices, and goods and serviceofferings of an entity. For example, the dictionary may include theterms shipping, quality, décor, design, reliability, returns, andcustomer service. As a more complete example of identifying commonality,commonality may be identified when the phrase “free shipping” or “freshingredients” is repeated a sufficient number of times for the group ofentities that are in the top 10 percentile of an index. Such commonalityidentifies aspects, practices, or goods and service offerings that canpositively impact or improve the credibility of an entity. Othercommonality may be identified by analyzing other retrieved credibilitydata besides the qualitative credibility data. For example, commonalitywithin the demographic information of those submitting the credibilitydata may be identified (i.e., credibility data producers). Specifically,the indexer may identify that a majority of positive credibility datafor the entities in the group is originated by persons 30-40 years ofage and that a majority of negative credibility data for the entities inthe group is originated by persons 18-29 years of age. As still anotherexample, commonality may also identify that a certain threshold numberof successful entities actively use social media networks. Suchidentification may be made based on how much credibility data isaggregated from social media data sources for the successful entitiesrelative to other entities. Specifically, if entities in the top fivepercentile receive and post on average hundreds of social mediaresponses per week and all other entities receive and post on averageten or fewer social media responses, then the indexer can identify fromthe aggregated data that having an active social media presence is asuccessful practice.

After analyzing the retrieved credibility data for commonality andidentifying the commonality, the process classifies (at 2350) theidentified commonality as a successful practice or as an unsuccessfulpractice. The classification is determined based on the entities fromwhich the commonality is derived. If the entities from which thecommonality is identified are top performing entities in a particularindex, then the process classifies such commonality as successfulpractice(s) that can potentially improve the credibility of theparticular entity if enacted by the particular entity. Conversely, ifthe entities from which the commonality is identified are bottomperforming entities in a particular index, then the process classifiessuch commonality as unsuccessful practice(s) that can potentiallydegrade the credibility of the particular entity if enacted by theparticular entity. The process presents (at 2360) the commonality basedon the classifications as successful practices or unsuccessfulpractices. The identified commonality or identified successful andunsuccessful practices may be stored in the database for subsequentpresentation to an interested user and may be stored in conjunction withthe index from which the practices were identified such that thepractices can be accessed by drilling down from the index. In someembodiments, the interface portal formats the practices for presentationto users in a web browser or other network enabled application. In somesuch embodiments, users can submit a query to identify an entity ofinterest and then click on a link to access the successful andunsuccessful practice information identified for that entity based onprocess 2300.

From the examples above, the identified commonality may reveal that ifthe particular entity implemented a free shipping program it will beemploying a practice used by its more successful competitors. Similarly,the identified commonality may reveal that if the particular entity usedfresh ingredients it will distinguish itself from its poor performingcompetitors.

In summary, the indexer automatedly identifies successful practices fora particular entity by identifying commonality in the aggregatedcredibility data of entities that rank in a top specified percentile ofone or more indices related to the particular entity. Similarly, theindexer automatedly identifies unsuccessful practices for a particularentity by identifying commonality in the aggregated credibility data ofentities that rank in a bottom specified percentile of one or moreindices related to the particular entity.

In some embodiments, the identified successful and unsuccessfulpractices are converted into monetized tangible assets whereby access tosuch information is restricted to paying users or users that are membersin a subscription plan. When access is restricted, users either registerwith the credibility scoring and reporting system by providing logininformation or by providing payment information using the interfaceportal to gain access to the identified successful and unsuccessfulpractices. Therefore, entities that are interested in identifying whatsuccessful competitors are doing to achieve their advantage in themarketplace can do so by purchasing access to these assets. With accessto these assets, entities can perform targeted change to their ownpractices based on the identification of practices that have been provento improve or degrade the credibility of others. Specifically, theentity can change its own existing practices, implement new practices,or remove existing practices to better conform their operation to theoperation of their more successful competitors. In this manner, thetangible assets that identify successful and unsuccessful practices canbe used to improve the credibility for a particular entity andultimately its revenue generation capabilities in a targeted or directedmanner by identifying what practices the entity should enact to improveits credibility and what practices the entity should avoid to preventdamage to its credibility.

In some embodiments, the indexer directly identifies successful andunsuccessful practices enacted by the entity of interest in addition toor instead of identifying the successful and unsuccessful practices ofcompetitors or other entities related to the entity of interest. In thismanner, the indexer targets the identified practices specifically to thecurrent and actual practices used by the entity as opposed to thepractices that others have used.

FIG. 24 presents a process 2400 performed by the indexer for identifyingsuccessful and unsuccessful practices of a particular entity inaccordance with some embodiments. As in process 2300 of FIG. 23, steps2405-2440 of process 2400 mirror steps 2305-2340 of process 2300. Inother words, the process 2400 begins by identifying (at 2405) theparticular entity of interest, selecting (at 2410) at least one indexassociated with the particular entity, grouping (at 2420) some entitiesassociated with the index to at least one group, retrieving (at 2430)aggregated credibility data for the grouped entities, and analyzing (at2440) the credibility data to identify commonality within the group.

However, process 2400 performs the additional steps 2450, 2460, and2470. Namely, the process retrieves (at 2450) the credibility data thatwas aggregated for the identified particular entity. Then, the processidentifies (at 2460) any credibility data from the retrieved credibilitydata of the particular entity that has commonality with credibility datafrom the group that satisfied the commonality threshold. Credibilitydata of the particular entity that has commonality with credibility dataof the group of entities is identified as a successful practice when thegroup of entities represents successful entities of the selected indexand credibility data of the particular entity that has commonality withcredibility data of the group of entities is identified as anunsuccessful practice when the group of entities represents unsuccessfulentities of the selected index. The process then presents (at 2470) theidentified credibility data of the particular entity having commonalitywith the group such that the identified credibility data is specific tothe particular entity and targets practices of the particular entity.

In summary, the practices of an identified entity are compared againstpractices of its competitors or other related entities (1) in order toidentify which practices are performed in common between the identifiedentity and a set of top performing entities or entities with highcredibility where these practices are identified as successful practicesand (2) in order to identify which practices are performed in commonbetween the identified entity and a set of poor performing entities orentities with low credibility where these practices are identified asunsuccessful practices.

In some embodiments, the identified practices are utilized by theindexer to predict how a change to an business practice will positivelyor negatively affect the credibility score of an entity. To do so, theindexer identifies an average credibility score contribution provided bya practice, where the practice has been quantified into a quantitativemeasure that is included as part of the computed credibility scores forthese various entities. FIG. 25 presents a process 2500 performed by theindexer for predicting the credibility score contribution of aparticular practice to a credibility score in accordance with someembodiments.

The process 2500 begins by selecting (at 2510) a practice for which apredicted credibility score contribution is desired. In someembodiments, the practice is selected by a user when the user ispresented with the successful and unsuccessful practices that areidentified using the processes described with reference to FIGS. 23 and24. In some embodiments, the interface portal provides functionalitywhereby the user can select identified practices in order to view thepredicted impact that implementing that selected practice will have onthe credibility score of the user's business. For example, as shown inFIG. 26, the interface portal presents a set of practices that areidentified for an entity (e.g., 2610 and 2620). A particular practicefrom the set of practices can be selected using a pointing tool (e.g.,mouse pointer). Once selected the particular practice is selected, theaverage contribution of that particular practice to the credibilityscores of entities implementing that particular practice is displayed inwindow 2630.

With reference back to process 2500, after a practice is selected, theprocess identifies (at 2515) the set of entities that have previouslyimplemented that practice. The process then retrieves (at 2520) thecredibility data that was aggregated for the identified set of entitiesand that was used in identifying the selected practice. Steps 2515 and2520 involve a reverse lookup. Specifically, these steps involveidentifying the commonality that is the basis for the practice,identifying the entities that have implemented that practice, and thenidentifying the credibility data from which the commonality was derived.

The process identifies (at 2530) the quantitative measure(s) for theretrieved credibility data. Derivation of quantitative measures fromcredibility data is described above with reference to FIGS. 7-11.

Next, the process obtains (at 2540) the credibility scores for the setof entities associated with the selected practice and computes (at 2550)the average contribution of the quantitative measures to the credibilityscore of each entity in the set of entities. The average contributionvalue will be stored to the database and will be presented as thepredicted credibility contribution that the selected practice will haveto the credibility score of an entity that implements that selectedpractice.

FIG. 27 conceptually illustrates using process 2500 to predict thecredibility contribution for a selected practice in accordance with someembodiments. The figure includes (1) a practice selection interface2710, (2) credibility data 2720, 2725, and 2730 that is associated witha set of entities 2735, 2740, and 2745 used in the identification of theselected practice, (3) quantitative measures 2750, 2755, and 2760 thatare derived from the credibility data 2720-2730, (4) a set ofcredibility scores 2765, 2770, and 2775 associated with each entity ofthe set of entities 2735-2745, (5) percent contributions 2780, 2785, and2790 that each quantitative measure 2750, 2755, and 2760 makes to itsassociated entity's credibility score, and (6) the average predictedcredibility contribution 2795 that is computed from the percentcontributions 2780-2790.

The practice selection interface 2710 is one of many interfacesgenerated by the interface portal of some embodiments and presented toentities so that they can better manage their credibility. Specifically,interface 2710 allows entities to see what practices successful andunsuccessful competitors or related entities have implemented.Additionally, interface 2710 allows entities to see what impactimplementing a particular practice would have on their credibilityscores. In some embodiments, entities access and interact with theinterface 2710 using a web browser application that is directed to acredibility scoring and reporting system website (e.g.,www.credibility.com/practices). A selection is made by moving a pointingtool, such as a mouse cursor, over a graphical representation of apractice that is displayed in the interface 2710. Then by clicking onthe graphical representation or using other input means (e.g., touchinputs, keyboard inputs, etc.), the practice identified by the graphicalrepresentation is selected. In FIG. 27, the pointing tool is placed overthe graphical representation for practice 2715 and a selection is made.

As described above with reference to FIG. 25, once the selection ismade, the indexer identifies the credibility data 2720, 2725, and 2730that is associated with the set of entities 2735, 2740, and 2745 used inthe identification of the selected practice 2715. In this figure, thecredibility data 2720-2730 is in the form of independent but relatedaggregated qualitative user provided reviews. Next, the indexeridentifies the credibility scores 2765-2775 for the entities 2735-2745.The credibility scores 2765-2775 are processed with the quantitativemeasures 2750-2760 that are derived from the credibility data 2720-2730in order to determine the percentage that each credibility score2765-2775 is affected by a corresponding quantitative measure. As oneexample, the quantitative measure 2750 for the selected practice 2715affects the overall credibility score 2765 of the “Acme” business 2735by 4.25%.

By averaging the percentage contributions 2780, 2785, and 2790, theindexer computes the average credibility score contribution 2795. Theaverage credibility score contribution 2795 can then be presented toentities that are interested in knowing how much their credibility willchange if they were to implement the selected practice as one of theirown practices. In FIG. 27, an entity that implements a “free shipping”or equivalent practice will, on average, receive a 3.82% improvement intheir credibility.

D. Lead Generation

In some embodiments, the indexer automatically provides a particularentity with leads to new partnerships that if established can improvethe credibility of the particular entity. Specifically, the credibilityof an entity can be affected by its partners where the partners providegoods or services to the entity and those goods and services affect thequality of goods and services offered by the entity. For example, abusiness that provides goods often requires suppliers to supply thebusiness with raw materials or component goods. When these suppliedgoods are of low or sub-par quality, the credibility of the businesswill likely be adversely affected because of: a higher failure rate ofgoods, a higher proportion of goods being returned after purchase, ahigher number of calls to customer support, and, in general, loweredgoodwill. Conversely, the credibility of a business that sourcescomponents for its goods from trusted or high quality suppliers willlikely be positively affected because of generated goodwill that resultsfrom higher quality goods or services. Besides suppliers, other partnersthat can directly or indirectly affect the credibility of an entityinclude financiers, logistics providers, manufacturers, marketingagencies, and contractors as some examples.

The leads generated by the indexer therefore contain intrinsic valuebased on their potential to improve the credibility of the entity and,in so doing, improve the revenue generation capabilities of the entity.As such, the identified leads are part of the set of tangible assetsprovided by the credibility scoring and reporting system. By restrictingaccess to these leads and by tailoring the leads to match the specificneeds of a particular entity, the credibility scoring and reportingsystem can commoditize and monetize these assets. A tangentiallybeneficial feature is that the indexer can directly identify currentpartners of an entity that detrimentally affect the credibility for thatentity. In some instances, the entity may be wholly unaware that one ormore of its partners is actually hurting the credibility of the entity.By specifically identifying these partners to an entity and bysuggesting better alternative partners that are in establishedrelationships with competitors with top-tier credibility, the indexeridentifies targeted actions that the entity can take to improve itscredibility and its revenue.

FIG. 28 presents a process 2800 that is in accordance with someembodiments and that is performed by the indexer to identify for aparticular entity the beneficial and detrimental partners of itscompetitors or of related entities. The process 2800 begins byidentifying (at 2805) the particular entity. The process selects (at2810) at least one index that is associated with the identifiedparticular entity. In some embodiments, the index is selected usinglinks that associate one or more indices to the particular entity. Insome embodiments, the process by default selects a credibility scoringindex that identifies the credibility score of the identified particularentity relative to its competitors.

As in FIG. 23, the process groups (at 2820) some of the entities in theselected index into at least one group. For example, the process groupsthe entities that are identified to be in the top percentile of theindex in a group or the process groups the entities that are identifiedto be in the bottom percentile of the index in a group. It should beapparent to one of ordinary skill in the art that more than one groupcan be identified at step 2820 and that the process 2800 can be adaptedto generate leads from each of the groups where each group isrepresentative of a different performance class.

The process classifies (at 2830) the entities in the group. In someembodiments, the classification is based on the position of the entitiesin the index. Next, the process traverses (at 2840) the establishedpartnerships for each of the entities in the specified groupings. Thispartnership information may be user provided when a user registers anentity or the partnership information may be automatically identifiedbased on aggregated credibility data from partnership sites such as wwwlinkedin.com, www.spoke.com, and the like. In some embodiments, thepartnership information is contained in the credibility reports or datastructures of the database as described with reference to FIG. 5 above.In some embodiments, the partnership information includes at least firstdegree partners and second degree partners. A first degree partner foran entity is one who is in a direct relationship with that entity. Forexample, a first degree partner includes a supplier who ships goodsdirectly to the entity. A second degree partner is one who is in anindirect relationship with the entity. For example, a second degreepartner includes a supplier who provides raw materials to a componentsupplier that then uses the raw materials to produce component goodsthat are directly shipped to the entity.

The process presents (at 2850) the partners for the entities of theidentified group as generated leads for the particular entity. Forexample, the partners that are identified from a group of top-tier indexperforming entities are presented as partners that can potentiallyimprove upon the credibility score of the particular entity if theparticular entity was to engage in a relationship with those partners.The partners that are identified from a group of bottom-tier indexperforming entities are presented as partners that can potentiallydegrade the credibility score of the particular entity if the particularentity were to engage in a relationship with those partners. Thepresented partners will be relevant to the identified particular entity,because the index selected at 2810 will include other entities that arerelated to the identified particular entity based on at least onedimension. The leads may be presented through the interface portal ormay be stored to the database for subsequent presentation based on upona user submitted query or by navigation through the websites of theinterface portal.

In some embodiments, the partnerships that are presented to theparticular entity are filtered to present only those partners that arein established relationships with a threshold number of related entities(e.g., two or more). Additionally or alternatively, the presentedpartners can be filtered based on the credibility scores of thepartners. For example, the indexer identifies 15 partners from the topperforming competitors of the particular entity. The indexer thenfilters these 15 partners to identify and present five of the 15partners with the highest overall credibility scores as leads for theparticular entity.

FIG. 29 conceptually illustrates using process 2800 to identify afiltered listing of partners of top performing entities in accordancewith some embodiments. The figure includes (1) an index 2910 associatedwith a selected entity, (2) a set of entities 2920, 2930, and 2940identified in the top 10% of the index, (3) first degree partners 2950,2955, 2960, 2965, 2970, 2975, and 2980 of the set of entities 2920-2940and the credibility scores for those partners, and (4) a filtered set ofpartners 2990 that includes partners 2955, 2960, and 2970 to provide asleads to the particular entity.

In accordance with process 2800, FIG. 29 illustrates identifying theindex 2910 that is associated with the selected entity. As shown, theindex 2910 presents credibility scores for restaurants in Malibu,Calif., wherein the selected entity is a business that ranks in the 83rdpercentile of the index 2910. The top performing entities of the indexare identified and include entities 2920, 2930, and 2940 which areranked in the top 10 percentile of the index 2910. For each entity 2920,2930, and 2940, the indexer identifies their respective first degreepartners. As shown, entity 2920 has first degree partners 2950, 2955,and 2960; entity 2930 has first degree partners 2960, 2965, and 2970;and entity 2940 has first degree partners 2970, 2975, and 2980. Itshould be noted that partner 2960 is a partner of entities 2920 and 2930and partner 2970 is a partner of entities 2930 and 2940.

Rather than provide all partners 2950-2980 to the selected entity, thelist of partners is filtered to provide the set of partners 2990 thatincludes partners 2955, 2960, and 2970. The set of partners 2990 isderived using a plurality of filtering rules. Specifically, theidentified partners with the highest credibility scores, partners 2955and 2960, are included in the set of partners 2990. Additionally,partner 2970 is included because it is a common partner of two of theidentified entities even though its credibility score is only the fifthhighest of all identified partners 2950-2980.

Partnership information that is presented in accordance with FIGS. 28and 29 identifies partners of successful competitors or related entitiesthat a particular entity can establish a relationship with in order toimprove upon its own credibility. However, this does not identify forthe particular entity whether its current partners are improving ordegrading the credibility of the particular entity. Accordingly, someembodiments utilize the partnership information gleaned from process2800 to provided targeted information to the particular entity. FIG. 30presents a process 3000 for providing targeted information regardingpartners of a particular entity in accordance with some embodiments.

Steps 3005-3030 are similar to steps 2805-2830 of FIG. 28. Specifically,the process identifies (at 3005) a particular entity and selects (at3010) at least one index that is associated with the particular entity.The process groups (at 3020) some of the entities in the index in atleast one of group. The process traverses (at 3030) the establishedpartnerships for the entities that were grouped.

Next, the process retrieves (at 3040) partnership information for theparticular entity. In some embodiments, this includes identifying thefirst degree partners of the particular entity. The process compares thepartners of the particular entity to the partners of the entities thatwere grouped to identify (at 3050) whether any of the partners of theparticular entity match any of the partners for the grouped entities.Depending on the classification of the entities in the group, eachpartner of the particular entity that matches a partner of a entity inthe group is presented (at 3060) according to the classification. Forexample, if the group of entities includes top tier index performingentities, any matching partners will be presented as partners thatbeneficially impact the credibility of the particular entity and if thegroup of entities includes bottom tier index performing entities, anymatching partners will be presented as partners that detrimentallyimpact the credibility of the particular entity.

Similar to the predictive credibility section above, the indexer can beleveraged to predictively identify for an entity what impact changing anexisting partner or adding a new partner would have on the credibilityscore of that entity. This determination is made by determining thecredibility score contribution that a specific partner makes to theoverall credibility score of an entity. The credibility scorecontribution for all entities that have the specific partner are thenaveraged to derive an average credibility score contribution that canthen be used to predict what impact establishing a partnership with thatspecific partner will have on the credibility score of an entity.

E. Preliminary Credibility

In some embodiments, the indexer uses information from the indices todetermine a preliminary credibility score for a new entity for whichcredibility data does not exist or has not yet been aggregated. A newentity may include one that recently came into existence or one that hasoperated “under the radar” because of its small size or lack ofexposure.

Agents of the new entity may register the entity with the credibilityscoring and reporting system using the interface portal so that thesystem can be made aware of the entity. During registration, variousinformation about the entity is provided by the registrant such as thename, address, phone number, agents/principals, field of business, goodsand services provided by the entity, etc. Using this information, apreliminary credibility score is determined and provided to theregistrant. The preliminary credibility score may also be provided tothe public when they search for the new entity using the interfaceportal. In some embodiments, the preliminary credibility score iscomputed based on credibility scores of identified competitors orrelated entity that are adjusted according to a set of factors.

The preliminary credibility score instantly and accurately identifiesthe credibility of the new entity. Moreover, this identification isperformed automatically by the indexer thereby eliminating individualsubjective biases and interpretation from the computation of thepreliminary credibility score. As actual credibility data is generatedfor the new entity and is aggregated into the credibility scoring andreporting system, the preliminary credibility score is adjusted toproduce a credibility score that more heavily accounts for thecredibility data that was aggregated for the entity.

FIG. 31 presents a process 3100 performed by the indexer to compute apreliminary credibility score for a new entity in accordance with someembodiments. The process 3100 begins by obtaining (at 3110) new entityregistration information. The registration is performed by a userdirecting a web browser application to an entity registration website ofthe credibility scoring and reporting system that is provided by theinterface portal. The entity registration website includes interactivefields in which the user enters entity information. In some embodiments,the entity information includes identification information such as thename, address, phone number, website, etc. of the entity. In someembodiments, the entity information also includes information regardingthe field of business that the entity operates in, goods and servicesprovided by the entity, and other such information from which the entitycan be classified and its competitors or other related entities can beidentified.

The registration information is utilized by the process to identify (at3120) potential competitors or other entities that are related acrossone or more dimensions. To do so, the indexer parses the registrationinformation to extract data useful in classifying the entity (e.g.,field of business, goods and services provided, zipcode, etc.). Theindexer then formulates a query based on the parsed information to passto the database. In response, the database identifies a list ofcompetitors or related entities. In some embodiments, the query servesto identify one or more dimensions for an index from which thecompetitors or related entities are identified.

The process computes (at 3130) the average credibility score for theidentified entities. Next, the process adjusts (at 3140) the averagecredibility score based on a set of factors to produce the preliminarycredibility score for the new entity. The set of factors adjust theaverage credibility score to better reflect the credibility of a newentity relative to other established entities that are competitors orotherwise related to the new entity. The set of factors are used toadjust the average credibility score to account for how crowded thefield of business is, how well established the competition is, locationof the new entity relative to competitors, desirability of the newentity location, demand for the goods or services of the new entity ascan be determined from the identified trends described above withreference to FIGS. 21 and 22, number of similar entities that haverecently failed, how “hot” the field in which the new entity operates inis, etc. For example, if an unknown fast-food restaurant was to open alocation directly adjacent to a McDonald's fast food restaurant, thenthe indexer can lower the preliminary credibility score to account forthis high level of local competition. However, if the same fast foodrestaurant was to open a location in a congested residentialneighborhood with no other fast food restaurants within a ten mileradius, then the indexer can increase the preliminary credibility scoreto account for the lack of competition.

It should be apparent that the set of factors can include additionalfactors in addition to or instead of some the above enumerated factors.Moreover, the indexer may selectively apply different factors to adjustthe preliminary credibility score of different entities. Also, theprocess can utilize more complicated algorithms to derive the basecredibility score that is subsequently adjusted. For example, instead oftaking the average credibility score for related entities, the processcan weight each credibility score differently based on how long theentity associated with that credibility score has been in operation.Less established or newer entities are likely to have credibility scoresthat more accurately reflect the credibility of the new entity for whicha preliminary credibility score is to be computed. Accordingly, thecredibility scores of the recently established entities will be weightedmore heavily than the credibility scores of the more established orolder entities.

The process associates (at 3150) the preliminary credibility score withthe new entity registration. The association may include storing thepreliminary credibility score with a data structure that was created forthe new entity. Additionally, the association may include presenting thepreliminary credibility score to the entity registrant at the end of theregistration process so that the registrant is immediately provided witha quantified measure of the entity's credibility.

In addition to or instead of using process 3100 to derive a preliminarycredibility score, some embodiments of the indexer parse user submittedregistration information about a new entity to identify components forwhich a credibility score may be derived based on historic credibilityfor that component. In some embodiments, a primary component used in thecomputation of the preliminary credibility score is the identificationof the principals operating the business entity. In many instances, theprincipals will have worked for other business entities for whichcredibility scores exist. By analyzing the historic performance of thecredibility scores for the previous business entities that a principalworked for, the indexer can determine whether that principal is likelyto have a beneficial or detrimental impact to the credibility of the newbusiness entity. For example, a new restaurant may be registered withthe credibility scoring and reporting system and the identificationinformation provided may identify the executive chef as havingpreviously been the executive chef at a Michelin star rated restaurant.Accordingly, the executive chef is acclaimed in the field and thereforewill likely beneficially contribute to the credibility score of the newbusiness. Similarly, a new franchise location that is operated byfranchisee with several other successfully performing location willreceive a higher preliminary credibility score for that new franchiselocation than a new franchise location that is operated by a franchiseewith no other franchise locations.

To obtain the historic credibility performance for registrationcomponents, the indexer accesses the database and from the storedcredibility data identifies previous associations for that component. Asone example, aggregated credibility data from social networking sites,such as www linkedin.com, can be used to identify previous businessesassociated with an individual. Then, by performing a lookup of thecredibility for those previous businesses, the indexer can determinewhether that individual is likely to have a beneficial or detrimentalimpact on the credibility score of a new business.

In some embodiments, the preliminary credibility score is commoditizedand monetized by restricting the preliminary credibility score to thoseentities that pay to gain access to the score. In this manner, thepreliminary credibility score is converted to a tangible asset withmonetary and sellable value.

The above methods for generating a preliminary credibility score aresimilarly applicable to generating a preliminary credit score for anewly formed business entity with no prior operational history. A newlyformed business entity is often highly dependent on lines of credit tobegin operations. However, lenders are more hesitant to extend credit tonewly formed business entities because they do not have establishedrecords from which their revenue generating capabilities can bedetermined. Therefore, a preliminary credit score is a valued asset forlenders interested in determining how much credit to extend to a newlyformed business entity.

As for the preliminary credibility score, the indexer computes apreliminary credit score by obtaining entity registration information.This information is utilized to identify potential competitors or otherentities that are related across one or more dimensions. This isaccomplished by the indexer parsing the registration information toextract data useful in classifying the entity (e.g., field of business,goods and services provided, zipcode, etc.). The indexer then identifiesentities that have commonality with the new entity based on the parseddata.

The indexer computes the average credit score for the identifiedentities and adjusts value based on the same set of factors describedabove with respect to the derivation of the preliminary credibilityscore. Additionally, credit information may be obtained based on pastperformance of various components identified in the registrationinformation including the past performance of the principals of the newentity.

F. Event Driven Credibility

In some embodiments, the indexer produces tangible assets in the form ofcredibility scores that not only account for credibility data that isassociated with an entity, but that also account for micro and macroevents. Micro and macro events include factors that are beyond theimmediate influence of the entity, but that nevertheless impact thecredibility of the entity. Such events may account for economicinfluences, political influences, climatic influences, seasonalinfluences, psychological influences, and logistical influences as someexamples. A specific example of an economic influence includesaccounting for whether an economy is in a “bull” market or “bear” marketand how that market impacts credibility of an entity. A specific exampleof a political influence includes whether the controlling governmentalparty (e.g., Republicans or Democrats) changes and how that changeimpacts credibility of an entity. A specific example of a climaticinfluence includes accounting for whether a natural disaster, such as atsunami or earthquake, has occurred and how that disaster impactscredibility of an entity. A specific example of a seasonal influenceincludes accounting for whether the holiday season is approaching andaccounting for how the time of year impacts the credibility of anentity. A specific example of a psychological influence includesaccounting for the public's perception of an industry, good, or serviceand how that perception impacts credibility of an entity. These examplesare not intended to be limiting or exhaustive, but are presented forillustrative purposes.

By accounting for such events, the indexer is able to produce a moreaccurate credibility score that can also serve to forecast potentiallyunforeseen or unknown credibility influences. For example, a smallbusiness entity may operate in a geographic region that is affected by anatural disaster. The small business entity may not have been directlyaffected by the natural disaster and may presume that its credibilitywill remain unaffected. However, other entities that utilize goods andservices of the small business entity may have been affected and thataffect can impact the credibility of the small business entity. Forexample, the credibility of the small business entity may bedetrimentally affected when the small business entity offers specialtyluxury items and the natural disaster temporarily reduces demand forsuch luxury items. As another example, the credibility of the smallbusiness entity may be positively affected when the small businessentity operates a hardware store and the natural disaster causes damageto the property of others which needs to be repaired using goods sold bythe small business entity. Based on the type and magnitude of the event,the indexer automatically adjusts the credibility of one or moreentities that are affected by the event or are otherwise impacted by theevent.

FIG. 32 presents a process 3200 performed by the indexer to adjustcredibility of entities based on the above enumerated and other microand macro events that are in accordance with some embodiments. Theprocess begins by identifying (at 3210) one or more events. Suchidentification occurs by the indexer aggregating news and social mediafeeds from multiple data sources and by processing the feeds to identifyreferences to the events. The news feeds may be obtained from onlinenews sites such as www.cnn.com and www.wsj.com as some examples. Thesocial media feeds may be obtained from social media sites such aswww.facebook.com and www.twitter.com as some examples. In someembodiments, the indexer identifies an event when processing of the oneor more feeds identifies a threshold number of references to that event.For example, should an earthquake occur on the west coast of the UnitedStates, news and social media references to the earthquake will increaseover time, crest, and decline. The surge in references about theearthquake will satisfy the threshold number of references needed toqualify the earthquake as an event. The identified event is matched (at3220) to one or more industries or entities that may potentially beaffected by the event. In some embodiments, matching occurs automatedlybased on identifiers that occur in the references and that areassociated with one or more entities. Specifically, some references tothe event may be embodied as text that includes identifiers foridentifying the geographic region affected by the event (e.g.,“earthquake in California”), the industry affected by the event (e.g.,“shortage of silicon used for transistors” or “crash in the bankingsystem”), etc. Natural language processing of the references identifiesthe identifiers. The identifiers are then compared with identifiers thatare associated with the entities of the credibility system. When a matchis found with a particular entity, the indexer designates the particularentity as one whose credibility is affected by the event. For example,when a trending event specifies “earthquake in California”, that eventmay automatically be associated with entities or indices that haveCalifornia as a geographic dimension. In some embodiments, systemadministrators can manually match events to particular industries,entities, or indices.

The indexer derives (at 3230) a quantitative measure that the matchedevent will have on the credibility scores of matched or associatedentities based on a determined magnitude of the event. In someembodiments, the magnitude is determined from natural languageprocessing of the identifiers that are associated with the event.Similar to the quantification performed at FIGS. 7-10, the aggregatedidentifiers may connote some degree of positivity or negativity that isconverted to a quantitative measure. For example, identifiers stating“worst natural disaster in the history of the state” or “estimated 5billion dollars in damages” can be processed to derive quantitativemeasures to impact credibility scores of the entities matched to eachevent. Additionally, system administrators can manually process theaggregated identifiers associated with an event to specify aquantitative measure or modify the quantitative measure that isautomatically provided by the indexer.

The process applies (at 3240) the quantitative measure to the matchedentities to adjust the credibility scores of those entities accordingly.In some embodiments, an event has a temporary impact on the credibilityscores of entities. The impact of the event is removed or degraded aftersome specified time whereby the significance of the event degrades or isno longer applicable. This specified time can be determined from thedecline in the amount of data that is aggregated with respect to a givenevent. A decay parameter may also be attributed to the event to prolongits impact on the credibility of entities even after the event hassubsided. For example, a decay parameter of one month may be applied toan earthquake event such that the credibility scores of entitiesimpacted by the earthquake will remain impacted for one month after theearthquake or after aggregated news and social media about theearthquake passes some minimum threshold.

In this manner, the credibility scoring and reporting system cantemporarily and automatically modify credibility scores of entities thatare affected by micro and macro level events. In so doing, users andentities are provided a more accurate and holistic presentation ofentity credibility.

In a similar manner, the credibility scoring and reporting system cantemporarily and automatically modify credit scores of affected entities.As part of the data aggregation performed by the master data manager,the credibility scoring and reporting system aggregates or derivescredit scores for the entities. The quantitative measure derived at 3230of process 3200 that quantifies the magnitude of the event can also beapplied to the credit scores of the affected entities. In so doing, thetemporary impact of the event to the creditworthiness of the entities isdetermined.

It should be noted that the automated means for impacting credibilityscores and impacting credit scores based on detected events is notlimited in scope or applicability and requires no manual intervention.Such a system is therefore able to automatedly detect events that occuranywhere in the world, automatedly identify the affected entitiesanywhere in the world whether directly affected or indirectly affectedthrough first or second degree partnerships, and automatedly provide ameasure that quantifiably identifies the impact the event has on thecredibility and creditworthiness of an identified entity. Moreover,because of the automation involved in the data aggregation, affectedentity identification, and impact determination such affects can be seenin real-time as the event occurs or immediately thereafter.

G. Credibility Management

In some embodiments, the credibility scoring and reporting systemprovides tools to facilitate passive credibility management. Passivecredibility management is useful for an entity that does not wish toactively monitor its credibility. Using the provided tools, the entitycan setup various alerts using the interface portal that become activewhen specified thresholds are satisfied. Alerts may be specified for theoverall credibility score, components used in deriving the credibilityscores, for specific dimensions of credibility, or combinations thereof.For example, an alert may be set to become active (1) when thecredibility score of the entity falls below a certain specified value,(2) when credibility data that negatively affects the credibility of theentity is aggregated and used in deriving the credibility of the entity,and/or (3) when the credibility of the entity falls to a specifiedpercentile in an index that is based on a particular geographicdimension of credibility. In some embodiments, alerts are provided inthe form of an email, telephone message, text message, fax, or socialmedia update. This listing is not intended to be exhaustive or limiting,but is presented for illustrative purposes.

FIG. 33 presents a process 3300 performed by the credibility scoring andreporting system to passively monitor credibility of an entity inaccordance with some embodiments. The process 3300 begins by setting (at3305) one or more specified credibility thresholds for an entity. Asnoted above, these thresholds can apply to credibility scores,credibility components, various dimensions of credibility, or anycombination thereof. Setting the thresholds includes defining automatedrules in the database that are periodically executed by a backgroundprocess running in the credibility scoring or reporting system. In someembodiments, defining a rule involves selecting one or more credibilitydimensions or scores to monitor and a triggering action for the selecteddimensions or scores. One or more interactive websites of the interfaceportal allow these thresholds to be set.

The process monitors (at 3310) the credibility of the entity accordingto the set thresholds. In some embodiments, monitoring the credibilityof the entity includes monitoring for changes or updates to thecredibility of the entity. This may include aggregating new credibilitydata that references the entity from the various data sources andadjusting the credibility score of the entity accordingly. Themonitoring may be performed on a continual basis as new credibility datafor the entity is aggregated. In some embodiments, the monitoring isperformed periodically. For instance, the system monitors thecredibility of the entity once a day to determine daily fluctuations tothe credibility of the entity.

The process determines (at 3320) whether the credibility of the entitysatisfies any of the set thresholds for that entity. If not, the processdetermines (at 3350) whether to continue monitoring and return to step3305 or whether the process should end and be restarted at somesubsequent time. When the credibility of the entity satisfies a setthreshold, the process identifies (at 3330) credibility data that causedthe threshold to be satisfied. The entity is then alerted (at 3340) ofthe threshold being satisfied and the alert optionally may include theidentified credibility data such that the entity is made aware of whatcaused the threshold to be satisfied. The process determines (at 3350)whether to continue monitoring and return to step 3305 or whether theprocess should end and be restarted at some subsequent time.

IV. Computer System

Many of the above-described processes and modules are implemented assoftware processes that are specified as a set of instructions recordedon a non-transitory computer-readable storage medium (also referred toas computer-readable medium). When these instructions are executed byone or more computational element(s) (such as processors or othercomputational elements like ASICs and FPGAs), they cause thecomputational element(s) to perform the actions indicated in theinstructions. Computer and computer system are meant in their broadestsense, and may include any electronic device with a processor includingcellular telephones, smartphones, portable digital assistants, tabletdevices, laptops, and netbooks. Examples of computer-readable mediainclude, but are not limited to, CD-ROMs, flash drives, RAM chips, harddrives, EPROMs, etc.

FIG. 34 illustrates a computer system with which some embodiments areimplemented. Such a computer system includes various types ofcomputer-readable mediums and interfaces for various other types ofcomputer-readable mediums that implement the various processes, modules,and engines described above (e.g., master data management acquisitionengine, reporting engine, interface portal, etc.). Computer system 3400includes a bus 3405, a processor 3410, a system memory 3415, a read-onlymemory 3420, a permanent storage device 3425, input devices 3430, andoutput devices 3435.

The bus 3405 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of thecomputer system 3400. For instance, the bus 3405 communicativelyconnects the processor 3410 with the read-only memory 3420, the systemmemory 3415, and the permanent storage device 3425. From these variousmemory units, the processor 3410 retrieves instructions to execute anddata to process in order to execute the processes of the invention. Theprocessor 3410 is a processing device such as a central processing unit,integrated circuit, graphical processing unit, etc.

The read-only-memory (ROM) 3420 stores static data and instructions thatare needed by the processor 3410 and other modules of the computersystem. The permanent storage device 3425, on the other hand, is aread-and-write memory device. This device is a non-volatile memory unitthat stores instructions and data even when the computer system 3400 isoff. Some embodiments of the invention use a mass-storage device (suchas a magnetic or optical disk and its corresponding disk drive) as thepermanent storage device 3425.

Other embodiments use a removable storage device (such as a flash drive)as the permanent storage device. Like the permanent storage device 3425,the system memory 3415 is a read-and-write memory device. However,unlike the storage device 3425, the system memory is a volatileread-and-write memory, such as random access memory (RAM). The systemmemory stores some of the instructions and data that the processor needsat runtime. In some embodiments, the processes are stored in the systemmemory 3415, the permanent storage device 3425, and/or the read-onlymemory 3420.

The bus 3405 also connects to the input and output devices 3430 and3435. The input devices enable the user to communicate information andselect commands to the computer system. The input devices 3430 include,but are not limited to, any of a capacitive touchscreen, resistivetouchscreen, any other touchscreen technology, a trackpad that is partof the computing system 3400 or attached as a peripheral, a set of touchsensitive buttons or touch sensitive keys that are used to provideinputs to the computing system 3400, or any other touch sensing hardwarethat detects multiple touches and that is coupled to the computingsystem 3400 or is attached as a peripheral. The input devices 3430 alsoinclude, but are not limited to, alphanumeric keypads (includingphysical keyboards and touchscreen keyboards) and pointing devices (alsocalled “cursor control devices”). The input devices 3430 also includeaudio input devices (e.g., microphones, MIDI musical instruments, etc.).The output devices 3435 display images generated by the computer system.The output devices include printers and display devices, such as cathoderay tubes (CRT) or liquid crystal displays (LCD).

Finally, as shown in FIG. 34, bus 3405 also couples computer 3400 to anetwork 3465 through a network adapter (not shown). In this manner, thecomputer can be a part of a network of computers such as a local areanetwork (“LAN”), a wide area network (“WAN”), or an Intranet, or anetwork of networks, such as the Internet. For example, the computer3400 may be coupled to a web server (network 3465) so that a web browserexecuting on the computer 3400 can interact with the web server as auser interacts with a GUI that operates in the web browser.

As mentioned above, the computer system 3400 may include one or more ofa variety of different computer-readable media. Some examples of suchcomputer-readable media include RAM, ROM, read-only compact discs(CD-ROM), recordable compact discs (CD-R), rewritable compact discs(CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layerDVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM,DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards,micro-SD cards, etc.), magnetic and/or solid state hard drives, ZIP®disks, read-only and recordable blu-ray discs, any other optical ormagnetic media, and floppy disks.

While the invention has been described with reference to numerousspecific details, one of ordinary skill in the art will recognize thatthe invention can be embodied in other specific forms without departingfrom the spirit of the invention. Thus, one of ordinary skill in the artwould understand that the invention is not to be limited by theforegoing illustrative details, but rather is to be defined by theappended claims.

We claim:
 1. A method for automated credibility management, the methodcomprising: providing an application to a particular entity forinstallation on a network enabled device of the particular entity;continually receiving over the Internet from a plurality of online sitesat a credibility management system machine, reviews and ratings aboutexperiences and accounts of others with the particular entity, themachine comprising a microprocessor and memory storing at least onethreshold, wherein the microprocessor updates a score quantifyingcredibility of the particular entity based on sentiment expressed insaid reviews and ratings; monitors the score in relation to a thresholdas said score changes because of said updates; generates an alert inresponse to a change in the score causing said score to pass thethreshold; transmits the alert over the Internet to the network enableddevice, wherein the alert activates the application to display on thenetwork enabled device a notification that credibility of the particularentity has passed the threshold.
 2. The method of claim 1 furthercomprising continually receiving over the Internet from a plurality ofonline sites at the credibility management system machine, reviews andratings about experiences and accounts of others with a subset ofentities that are related to the particular entity according to aspecified filter.
 3. The method of claim 2, wherein the microprocessorfurther identifies a positively reviewed or rated business practice inthe reviews and ratings directed to the subset of entities.
 4. Themethod of claim 3, wherein the alert further presents the positivelyreviewed or rated business practice as a suggested business practice forimproving the particular entity credibility score.
 5. The method ofclaim 1 further comprising receiving a set of filters provided by theparticular entity using the application to the credibility managementsystem machine, wherein the set of filters specify at least one of anoperational industry and geographic region.
 6. The method of claim 5,wherein the microprocessor further filters a subset of entities from aplurality of entities using the set of filters, wherein the subset ofentities includes entities from the plurality of entities that operatewithin the operational industry or geographic region specified in theset of filters.
 7. The method of claim 6, wherein the microprocessorfurther generates an index presenting the particular entity credibilityscore relative to credibility scores of the subset of entities.
 8. Themethod of claim 7, wherein said alert further activates the display topresent the index with a set of interactive tools enabling theparticular entity to adjust the set of filters, wherein adjusting theset of filters modifies the index to present the particular entitycredibility score relative to the credibility scores of a differentsecond subset of entities from the plurality of entities that satisfythe adjusted set of filters.
 9. The method of claim 1 further comprisingreceiving a value for the threshold from the application over theInternet at the machine, wherein the value is entered by the particularentity to the application.
 10. The method of claim 1, whereintransmitting the alert comprises sending at least one of an email andtext message over the Internet to the application.
 11. The method ofclaim 1, wherein the microprocessor performs natural language processingof the reviews, and wherein generating the alert comprises providing anotification of a review containing negative sentiment directed to theparticular entity or a practice of the particular entity.
 12. A methodcomprising: providing an application to a particular entity forinstallation on a network enabled device of the particular entity, theapplication displaying a graphical user interface (GUI) with aninteractive tool controlling a value for a filter; continually receivingover the Internet from a plurality of online sites at a credibilitymanagement system machine, reviews and ratings about experiences andaccounts of others with the particular entity, the machine comprising amicroprocessor and memory storing at least one threshold, wherein themicroprocessor, obtains a credit score of the particular entity and acredit score for each entity of a set of entities that satisfy thefilter value; generates an alert comprising an index presenting theparticular entity credit score in relation to credit scores of the setof entities; transmits the alert over the Internet to the networkenabled device, wherein the alert updates the application GUI to displaysaid index.
 13. The method of claim 12, wherein the microprocessorfurther generates a credit rating for the particular entity as asupplement to the credit score, wherein said generating comprisesadjusting the particular entity credit score according to the creditscores of the set of entities, wherein the microprocessor incrementallyincreases the particular entity credit rating based on a number ofentities from the set of entities having a lower credit score than theparticular entity credit score, and wherein the credit rating presentscreditworthiness of the particular entity relative to creditworthinessof each entity of the set of entities.
 14. The method of claim 13,wherein the alert further presents the particular entity credit ratingin place of or in addition to the credit score in the application GUI.15. The method of claim 12, wherein the filter specifies a geographicregion surrounding the particular entity, wherein the interactive tooldefines the geographic region surrounding the particular entity, andwherein the set of entities comprises entities operating within thegeographic region of the filter.
 16. The method of claim 12, wherein thefilter specifies an industry, and wherein the set of entities comprisesentities operating within the industry specified by the filter.
 17. Themethod of claim 12, wherein the index is a graph plotting (i) aplurality of data points representing the credit score of each entity ofthe set of entities and (ii) an indicator identifying in the graph, thecredit score of the particular entity.
 18. A method comprising:providing a credibility or credit monitoring application to a particularentity for installation on a network enabled device of the particularentity; continually receiving references to events over the Internetfrom a plurality of online sites at a system machine, the machinecomprising a microprocessor and memory, wherein the microprocessor,identifies a threshold number of the references directed to a particularevent; processes the references directed to the particular event toidentify at least one of a geographic region or industry in which theparticular entity operates influenced by the particular event;determines a magnitude of the event based on sentiment expressed withinthe references directed to the particular event; adjusts one of acredibility score or credit score of the particular entity based on themagnitude of the particular event; transmits an alert over the Internetto the network enabled device, wherein the alert activates theapplication to display a change to one of the particular entitycredibility score or credit score as a result of said adjustingaccording to the magnitude of the particular event.
 19. The method ofclaim 18, wherein said identifying comprises identifying a thresholdnumber of negative references to the geographic region or industry inwhich the particular entity operates, and wherein said adjustingcomprises decreasing the credibility score or the credit score of theparticular entity based on the magnitude of the negative references tothe particular event.